Operational and environmental performance measures in a multi-product closed-loop supply chain

This paper investigates a number of operational and environmental performance measures, in particular those related to transportation operations, within a closed-loop supply chain. A mathematical model in the form of a linear programming formulation is used to model the problem, which captures the trade-offs between various costs, including those of emissions and of transporting commodities within the chain. Computational results are presented for a number of scenarios, using a realistic network instance.<br/

Modeling and optimizing the integrated problem of closed-loop supply chain network design and disassembly line balancing

This paper describes an integrated model that jointly optimizes the strategic and tactical decisions of a closed-loop supply chain (CLSC). The strategic level decisions relate to the amounts of goods flowing on the forward and reverse chains. The tactical level decisions concern balancing disassembly lines in the reverse chain. The objective is to minimize costs of transportation, purchasing, refurbishing, and operating the disassembly workstations. A nonlinear mixed integer programming formulation is described for the problem. Numerical examples are presented using the proposed model

Mixed model disassembly line balancing problem with fuzzy goals

The collection of used products is the driving force of remanufacturing systems and enterprises can gain significant economic, technical and social benefits from recycling. All products are disassembled up to some level in remanufacturing systems. The best way to disassemble returned products is valid by a well-balanced disassembly line. In this paper, a mixed integer programming (MIP) model is proposed for a mixed model disassembly line balancing (MMDLB) problem with multiple conflicting objectives: (1) minimising the cycle time, (2) minimising the number of disassembly workstations and (3) providing balanced workload per workstation. In most real world MMDLB problems, the targeted goals of decision makers are frequently imprecise or fuzzy because some information may be incomplete and/or unavailable over the planning horizon. This study is the first in the literature to offer the binary fuzzy goal programming (BFGP) and the fuzzy multi-objective programming (FMOP) approaches for the MMDLB problem in order to take into account the vague aspirations of decision makers. An illustrative example based on two industrial products is presented to demonstrate the validity of the proposed models and to compare the performances of the BFGP and the FMOP approaches

Public Health Rep

19314547PMCnul

The Implications Of Carbon Pricing In Australia: An Industrial Logistics Planning Case Study

This article investigates the cost implications and carbon reduction potentials of the carbon-pricing scheme in Australia. A non-linear optimization model is developed representing the trade-off between transportation costs and the costs of carbon emission and fuel consumption. The latter are expressed as functions of vehicle traveling speed and road roughness. Piecewise functions and tangent plane approximation are adopted to linearize the developed model for implementation in CPLEX. Empirical findings from model implementation in an actual case study suggest that the current carbon-pricing scheme in Australia may only make a minor increase in the overall logistics costs that may be inadequate to drive a significant shift in transport behaviors

Minimizing the Maximum Processor Temperature by Temperature-Aware Scheduling of Real-Time Tasks

Thermal management is gaining importance since it is a promising method for increasing the reliability and lifespan of mobile devices. Although the temperature can be decreased by reducing processor speed, one must take care not to increase the processing times too much; violations of deadline constraints must be prevented. This article focuses on the tradeoff between performance and device temperature. We first analyze this tradeoff and show how to determine the optimal lower bound for the maximum temperature for a given set of jobs with known workloads and deadlines. To do so, we use a thermal model, which describes how future decisions impact temperature dynamics. Then, we introduce a processor scheduling algorithm that computes the resource allocation that achieves this lower bound. Consequently, our algorithm finds the optimal resource allocation for the purpose of minimizing the maximum processor temperature for a set of jobs with known workloads and deadlines. Our experimental validation shows that our thermal management algorithm can achieve a reduction of up to 15 °C (42%) of the maximum temperature when the workload is high, where a previously proposed method achieved a reduction of up to 10 °C (25%). Another advantage of our method is that it decreases the variance in the temperature profile by 16% compared to previously proposed methods

A Generic Processor Temperature Estimation Method

Most modern mobile embedded devices have the ability to increase their computational power typically at the cost of increased heat dissipation. This may result in temperatures above the design limit, especially if active cooling is inapplicable. Thus, it is necessary to consider processor temperature while scheduling tasks. This means estimating the change in temperature due to changed workload is crucial for high performance mobile embedded devices. To address this challenge, we first introduce a model to estimate the temperature and classify the system dependent model parameters. Then, to determine these parameters, we develop a new method, which can be applied on any mobile embedded device. The only requirement for our new method is learning the device characteristics by processing a certain task while recording the temperature with built-in sensors. Our results show that our method can achieve high accuracy within a short testing period