Supply chain network design (SCND) is the process for designing and modeling the supply chain such that to minimize the costs generated by the location of facilities and the flow of product between the selected facili...
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Supply chain network design (SCND) is the process for designing and modeling the supply chain such that to minimize the costs generated by the location of facilities and the flow of product between the selected facilities. The aim of this paper is to investigate a particular SCND, namely the two-stage supply chain network design problem with risk-pooling and lead times and to provide a novel efficient and effective genetic algorithm (GA) designed to fit the challenges of the considered optimization problem. Extensive computational experiments were performed on two sets of instances and the achieved results prove the performance of our proposed GA.
Today's large-scale parallel workflows are often processed on heterogeneous distributed computing platforms. From an economic perspective, computing resource providers should minimize the cost while offering high ...
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Today's large-scale parallel workflows are often processed on heterogeneous distributed computing platforms. From an economic perspective, computing resource providers should minimize the cost while offering high service quality. It has become well-recognized that energy consumption accounts for a large part of a computing system's total cost, and timeliness and reliability are two important service indicators. This work studies the problem of scheduling a parallel workflow that minimizes the system energy consumption under the constraints of response time and reliability. We first mathematically formulate this problem as a non-linear mixed integer programming problem. Since this problem is hard to solve directly, we present some highly-efficient heuristic solutions. Specifically, we first develop an algorithm that minimizes the schedule length while meeting reliability requirement, on top of which we propose a processor-merging algorithm and a slack time reclamation algorithm using a dynamic voltage frequency scaling (DVFS) technique to reduce energy consumption. The processor-merging algorithm tries to turn off some energy-inefficient processors such that energy consumption can be minimized. The DVFS technique is applied to scale down the processor frequency at both processor and task levels to reduce energy consumption. Experimental results on two real-life workflows and extensive synthetic parallel workflows demonstrate their effectiveness.
This paper considers the problem of patient scheduling and capacity planning for the vaccination process during the COVID-19 pandemic. The proposed solution is based on a non-linear mathematical modeling approach repr...
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This paper considers the problem of patient scheduling and capacity planning for the vaccination process during the COVID-19 pandemic. The proposed solution is based on a non-linear mathematical modeling approach representing the dynamics of an open Jackson Network and a Generalized Network. To test these models, we proposed three objective functions and analyzed different configurations of the process corresponding to various levels of the models' parameters as well as the conditions present in the case study. To assess the computational performance of the models, we also experimented with larger instances in terms of number of steps or stations used and number of patients scheduled. The computa-tional results show how parameters such as the minimum percentage of patients served, the maximum occupation allowed per station and the objective functions used have an impact on the configuration of the process. The proposed approach can support the decision-making process in vaccination centers to efficiently assign human and material resources to maximize the number of patients vaccinated while ensuring reasonable waiting times, number of patients in queue and servers' utilization rates, which in turn are key to avoid overcrowding and other negative conditions in the system that could increase the risk of infections.(c) 2022 Elsevier Ltd. All rights reserved.
Maximizing overall system reliability by identifying optimal system configuration considering several design constraints is known as reliability redundancy allocation problem (RRAP). Since reliability is an important ...
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Maximizing overall system reliability by identifying optimal system configuration considering several design constraints is known as reliability redundancy allocation problem (RRAP). Since reliability is an important quality attribute in critical systems, RRAP has been intensively investigated in the literature. In this paper, a new model of RRAP for heterogeneous and homogeneous components is developed. Our proposed model handles component mixing in subsystems under both active and cold-standby redundancy strategies. The problem, therefore, is to decide the number of components in each subsystem (redundancy level), the failure rate of selected components, and the type of redundancy strategy for each of them under multiple design constraints including system weight, cost, and volume. Since RRAP falls into the NP-hard category of engineering optimization problems, a teaching learning-based optimization (TLBO) algorithm is implemented to solve it. Finally, the simulation results of the proposed RRAP model by TLBO on three well-known benchmark problems are provided, followed by the comparisons with recent existing related works. The comparative results suggested the effectiveness of the proposed approach in finding the optimal system configuration with higher system reliability in all cases.
Forest biomass is one of the renewable sources of energy that has been used for generating electricity. The feasibility and cost of producing electricity from forest biomass depend on long term availability of biomass...
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Forest biomass is one of the renewable sources of energy that has been used for generating electricity. The feasibility and cost of producing electricity from forest biomass depend on long term availability of biomass, its cost and quality, and the cost of collecting, pre-processing, handling, transportation, and storage of forest biomass, in addition to the operating and maintenance costs of the conversion facility. To improve the cost competitiveness of forest biomass for electricity generation, mathematical programming models can be used to manage and optimize its supply chain. In this paper, the supply chain configuration of a typical forest biomass power plant is presented and a dynamic optimization model is developed to maximize the overall value of the supply chain. The model considers biomass procurement, storage, energy production and ash management in an integrated framework at the tactical level. The developed model is a nonlinearmixedintegerprogramming which is solved using the outer approximation algorithm provided in AIMMS software package. It is then applied to optimize the supply chain of a real biomass power plant in Canada. The optimum solution provides more profit compared to the actual profit of the power plant. Different scenarios for maximum available supply and also investment in a new ash recovery system were evaluated and the results were analyzed. The model in particular shows that investment in a new ash recovery system has economic as well as environmental benefits for the power plant. (C) 2012 Elsevier Ltd. All rights reserved.
Due to responding environmental issues, conforming governmental legislations and providing economic benefits, there has been a growing interest in recycling activities through the supply chains. Reverse supply chain (...
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Due to responding environmental issues, conforming governmental legislations and providing economic benefits, there has been a growing interest in recycling activities through the supply chains. Reverse supply chain (RSC) optimisation problem has a great potential as an efficient tactic to achieve this goal. While disassembly, one of the main activities in RSC, enables reuse and recycling of products and prevents the overuse, disassembly line balancing problem involves determination of a line design in which used products are partially/completely disassembled to obtain available components. The aim of this study is to optimise a RSC, involving customers, collection/disassembly centres and plants, that minimises the transportation costs while balancing the disassembly lines, which minimises the total fixed costs of opened workstations, simultaneously. A non-linearmixed-integerprogramming model, which simultaneously determines: (i) optimal distribution between the facilities with minimum cost, (ii) the number of disassembly workstations that will be opened with minimum cost, (iii) the cycle time in each disassembly centre and (iv) optimal assignment of tasks to workstations, is developed. A numerical example is given to illustrate the applicability of the proposed model. Different scenarios have been conducted to show the effects of sensitivity analyses on the performance measures of the problem.
A simple description of the structure and characteristics of a general multi-level reverse logistics network is presented firstly in this paper. Considering tree basic nodes in reverse logistics network including cust...
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A simple description of the structure and characteristics of a general multi-level reverse logistics network is presented firstly in this paper. Considering tree basic nodes in reverse logistics network including customer, initial collecting point and return center, a planning problem is proposed to locate return center, determining the number and periods of the required initial levels collecting points with the objective of minimizing the aggregate cost. Then, to reduce the solving complexity of the problem to some extent, some necessary presumptions are presented, based on which a nonlinearmixedintegerprogramming model is developed. Simultaneously, two assignment models are embedded. Denotation system and existing solving methods are described and discussed respectively. Furthermore, combined with a specific instance, a genetic algorithm (GA) is chosen to solve the problem and detailed solving process is listed as well. At last, by Matlab programming, the optimal solution to the instance is obtained and corresponding numerical analysis is carried out.
The central sales tax (CST) in India results in a differential sales tax structure. This contributes significantly to distribution network decisions that build logistics inefficiencies in firms operating in India. In ...
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The central sales tax (CST) in India results in a differential sales tax structure. This contributes significantly to distribution network decisions that build logistics inefficiencies in firms operating in India. In this paper, we develop a model for determining distribution centres (DCs) locations considering the impact of CST. A non-linearmixedinteger-programming problem that is formulated initially is approximated to a mixedinteger-programming problem. Using a numeric example, the effect of CST rates and product variety on DC locations is studied and found to be having impact. It is felt that the Indian Government proposal to switch over from the present sales tax regime to value added tax (VAT) regime would significantly contribute to reducing the logistics inefficiencies of Indian firms. (C) 2003 Published by Elsevier B.V.
The objective of this paper is the development of a design model for refrigerated automated storage and retrieval systems (R-AS/RS). Compared with ordinary unit-load AS/RS, the R-AS/RS under this study has several dif...
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The objective of this paper is the development of a design model for refrigerated automated storage and retrieval systems (R-AS/RS). Compared with ordinary unit-load AS/RS, the R-AS/RS under this study has several different design and operating characteristics: (1) greater emphasis is placed on the storage function and so it has a double-depth lane in the storage rack;(2) cooling units are required to maintain a cold temperature environment in the system;(3) the maximum number of storage orders handled per unit time is limited by the system capacity. Considering the above characteristics, the design problem is formulated as a non-linear mixed integer programming problem in which the cost of the system is minimized. The decision variables are the storage volume, the number of storage and retrieval (S/R) machines, the type and number of cooling units, and the physical configuration of the building. A case problem is solved to illustrate the model.
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