scholarly journals A robust-heuristic optimization approach to a green supply chain design with consideration of assorted vehicle types and carbon policies under uncertainty

2021 ◽  
Author(s):  
Zahra Homayouni ◽  
Mir Saman Pishvaee ◽  
Hamed Jahani ◽  
Dmitry Ivanov
Keyword(s):  
Supply Chains ◽  
Large Scale ◽  
Programming Model ◽  
Supply Chain Design ◽  
Heuristic Optimization ◽  
Optimization Approach ◽  
Economic Uncertainty ◽  
Cap And Trade ◽  
Sustainable Logistics ◽  
Goal Programming Model

AbstractAdoption of carbon regulation mechanisms facilitates an evolution toward green and sustainable supply chains followed by an increased complexity. Through the development and usage of a multi-choice goal programming model solved by an improved algorithm, this article investigates sustainability strategies for carbon regulations mechanisms. We first propose a sustainable logistics model that considers assorted vehicle types and gas emissions involved with product transportation. We then construct a bi-objective model that minimizes total cost as the first objective function and follows environmental considerations in the second one. With our novel robust-heuristic optimization approach, we seek to support the decision-makers in comparison and selection of carbon emission policies in supply chains in complex settings with assorted vehicle types, demand and economic uncertainty. We deploy our model in a case-study to evaluate and analyse two carbon reduction policies, i.e., carbon-tax and cap-and-trade policies. The results demonstrate that our robust-heuristic methodology can efficiently deal with demand and economic uncertainty, especially in large-scale problems. Our findings suggest that governmental incentives for a cap-and-trade policy would be more effective for supply chains in lowering pollution by investing in cleaner technologies and adopting greener practices.

scholarly journals Supply chain optimization with variable demand by considering financial criteria and scenarios

2017 ◽  
Vol 26 (44) ◽  
pp. 21 ◽  
Author(s):  
John Willmer Escobar
Keyword(s):  
Supply Chain ◽  
Large Scale ◽  
Net Present Value ◽  
Programming Model ◽  
Supply Chain Design ◽  
Mixed Integer ◽  
Supply Network ◽  
Distribution Centers ◽  
Supply Chain Optimization ◽  
Starting Point

This paper contemplates the supply chain design problem of a large-scale company by considering the maximization of the Net Present Value. In particular, the variability of the demand for each type of product at each customer zone has been estimated. As starting point, this paper considers an established supply chain for which the main problem is to determine the decisions regarding expansion of distribution centers. The problem is solved by using a mixed-integer linear programming model, which optimizes the different demand scenarios. The proposed methodology uses a scheme of optimization based on the generation of multiple demand scenarios of the supply network. The model is based on a real case taken from a multinational food company, which supplies to the Colombian and some international markets. The obtained results were compared with the equivalent present costs minimization scheme of the supply network, and showed the importance and efficiency of the proposed approach as an alternative for the supply chain design with stochastic parameters.

A Variables Clustering Based Differential Evolution Algorithm to Solve Multistage Goal Programming Model in Defense Projects Portfolio

2014 ◽  
Vol 1046 ◽  
pp. 367-370
Author(s):  
Yu Zhou ◽  
Yong Bin Li ◽  
Zhong Zheng Shi ◽  
Zheng Xin Li ◽  
Lei Zhang
Keyword(s):  
Differential Evolution ◽  
Goal Programming ◽  
Large Scale ◽  
Programming Model ◽  
Differential Evolution Algorithm ◽  
Solution Space ◽  
Pso Algorithm ◽  
Goal Programming Model ◽  
Evolution Algorithm ◽  
Improved Differential Evolution Algorithm

The multistage goal programming model is popular to model the defense projects portfolio optimization problem in recent years. However, as its high-dimensional variables and large-scale solution space, the addressed model is hard to be solved in an acceptable time. To deal with this challenge, we propose an improved differential evolution algorithm which combines three novel strategies i.e. the variables clustering based evolution, the whole randomized parameters, and the child-individual based selection. The simulation results show that this algorithm has the fastest convergence and the best global searching capability in 6 test instances with different scales of solution space, compared with classical differential evolution algorithm (CDE), genetic algorithm (GA) and particle swarm optimization (PSO) algorithm.

scholarly journals Modified Goal Programming Model for Limited Available Budget Allocation for Equipment Procurement under Inflation Condition

2020 ◽  
pp. 25-35
Author(s):  
O. O. Ojo ◽  
P. K. Farayibi ◽  
B. O. Akinnuli
Keyword(s):  
Goal Programming ◽  
Large Scale ◽  
Programming Model ◽  
Spare Parts ◽  
Strategic Decisions ◽  
Decision Tool ◽  
Multi Objective ◽  
Programming Tool ◽  
Goal Programming Model ◽  
Industrial Needs

Equipment procurement budget is of a great challenge in manufacturing industries by reasons of its multi-objectives, insufficient funds, and inflation problems. Solutions were proffered to these problems by identifying the strategic decisions required in equipment procurement (machine, accessories, spare parts and miscellaneous costs). Procurement changes from year to year based on equipment industrial needs. Hence eleven (11) scenarios for procurement but this study focused on a scenario where all the decisions are needed for procurement. This problem is multi-objective decision problem where there is need for multi-objective decision tool for its solution, therefore a goal programming tool was adopted and improved by integrating inflation model into it to be able to solve inflation problems. International Brewery Ilesha, Nigeria was used as case study for the model’s application to evaluate its performance. The strategic decisions deviated above or positively by 0.4604, 4.1311 and 2.3760 for machines, spare-parts and miscellaneous costs respectively while accessories cost was not deviated. Therefore, the procurement cost for Machines, Accessories, Spare-parts and Miscellaneous costs would be (N 166,015,000; $ 461,152.77), (N 127,968,000; $ 355,466.67), (N 548,075,000; $ 1,522,430.56), (N 271,091,500; $ 753,031.94). US Dollar exchange rate was at N 360 to a Dollar as at the time of this research. This multi-criteria decision tool will find its application useful in small, medium and large scale industries that equipment procurement budget affects their production.v

scholarly journals Optimization Approach for Yard Crane Scheduling Problem with Uncertain Parameters in Container Terminals

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Wenqian Liu ◽  
Xiaoning Zhu ◽  
Li Wang ◽  
Baicheng Yan ◽  
Xuewei Zhang
Keyword(s):  
Large Scale ◽  
Programming Model ◽  
Container Terminals ◽  
Small Scale ◽  
Optimization Approach ◽  
Basic Solution ◽  
Scheduling Problem ◽  
Crane Scheduling ◽  
Operation Efficiency ◽  
Yard Crane Scheduling

As the core operational issue in container terminals, yard crane scheduling problem directly affects the overall operation efficiency of port connecting highway or railway transportation and sea transportation. In practice, the scheduling of yard cranes is subject to many uncertain factors, so the scheme may be inapplicable and needs to be adjusted. From the perspective of proactive strategy, considering fluctuations in arrival time of external trucks as well as varied handling volume of yard cranes, a stochastic programming model is established in this paper to obtain a fixed scheme with the minimum expected value of yard crane makespan and total task waiting time over all the scenarios. The scheme does not require rescheduling when facing different situations. Subsequently, two algorithms based on certain rules are proposed to obtain the yard crane operation scheme in the deterministic environment, which are taken as the basic solution in the uncertain conditions, and then a tailored genetic algorithm is adopted to find the optimal solution with good adaptability to the uncertain scenarios. Finally, we use small-scale examples to compare the performance of algorithms in the deterministic and uncertain environment and then analyze the relationship between different yard crane configurations and the number of tasks. Large-scale experiments are performed to study the operation efficiency of the storage yard with different handling volumes assigned to each yard crane.

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