Multi-objective two-stage grey transportation problem using utility function with goals

Nowadays, transportation and logistics are considered as the drivers of economic development in the countries due to their impacts on the main variables of the country's economy such as production, employment, price, and the cost of living. Statistics indicate that fuel consumption constructs a major part of transportation costs, where its optimization leads to the creation of an energy‐efficient and sustainable transportation system. On the other hand, vehicles' traffic is also one of the main criteria affecting the travel time of vehicles between demand nodes in a supply chain, increasing fuel consumption, and, consequently, damaging effects of greenhouse gasses. In this paper, a novel robust mixed‐integer linear programming model is developed for a green vehicle routing problem with intermediate depots considering different urban traffic conditions, fuel consumption, time windows of services, and uncertain demand for perishable products. To validate and solve the suggested model, CPLEX solver of GAMS software is employed as an exact method. Finally, a case study problem is investigated to evaluate the applicability of the proposed model and determine the optimal managerial insights and policies in the real‐world conditions using sensitivity analyses. Moreover, a novel robustness threshold comparison is conducted to find the optimal level of budget assignment.

Section: Conclusion and Future Research Directionsmentioning

confidence: 99%

A robust green traffic‐based routing problem for perishable products distribution

Tirkolaee

Hadian

Weber

et al. 2019

“…It translates that a symbol of d (k) = E(k)/ x d (k) will not vary. So, w d (k) can be augmented for decrement of iterations number across the flat section that is shown in Equation (30). If convergence is to a concave plane, d (k) = E(k)/ x d (k) can be changed.…”

Section: Adaptive Inertia Weightmentioning

confidence: 99%

“…An efficient simulated annealing (SA) is proposed to solve a multitrip vehicle routing problem with time windows specifically related to urban waste collection . A Self‐ Learning Particle Swarm Optimization for Robust Multi‐Echelon Capacitated Location‐Allocation‐Inventory Problem is proposed by Babaee et al Roy et al introduced an algorithm and used the approach of revised multichoice goal programming to solve problems in many application areas of real‐life decision‐making problems. In another work, Roy et al explored the study of multichoice multi‐objective transportation problem (MCMTP) under the light of conic scalarizing function.…”

Section: Introductionmentioning

confidence: 99%

Employing a Gaussian Particle Swarm Optimization method for tuning Multi Input Multi Output‐fuzzy system as an integrated controller of a micro‐grid with stability analysis

Mir

Dayyani

Sutikno

et al. 2019

There are mainly two most essential problems in power networks, load frequency control and power flow management, which are grown recently because of growth in dimension/complication of grids. Present work suggests a controller based on fuzzy systems in which controller design is performed in a supervisory manner over a multiagent system aiming to control the frequency variation as well as generation cost minimization in the entire grid. The designing processes for low‐frequency controller (LFC) and management are mostly performed separately, which results in the disruption of both outputs. This challenge is tackled in this paper by the integration of them in the designing process. Additionally, stability guarantee is in high importance in the power systems, which is neglected in most of the related works. The Gaussian particle swarm optimization (GPSO) algorithm is applied for determining the optimal values of the decision variables, which can also guarantee the stability of the system by adopting a chaotic map by Gaussian function to balance the seeking abilities of particles that promotes the computation effectiveness without affecting the efficiency of the fuzzy controller. Then, the stability situationof the fuzzy + GPSO method is derived that guarantees a suitable global exploration and rapid convergence, with no require to gradients.

“…On the other hand, many of the information available in the field of SCM are limited to purchasing, procurement, inventory control, and marketing . In order to develop the link between supply chain decisions with a financial performance, the relationship between the operational and financial indices should be determined.…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, many of the information available in the field of SCM are limited to purchasing, procurement, inventory control, and marketing. [4][5][6] In order to develop the link between supply chain decisions with a financial performance, the relationship between the operational and financial indices should be determined. Identifying these indices will allow the master decision makers to transform financial goals into operational metrics, and then, middle-level managers can link the financial flow with the operational decision.…”

Section: Introductionmentioning

confidence: 99%

Multiobjective fuzzy mathematical model for a financially constrained closed‐loop supply chain with labor employment

Goli

Zare

Tavakkoli‐Moghaddam

et al. 2019

This paper addresses the multiobjective, multiproducts and multiperiod closed‐loop supply chain network design with uncertain parameters, whose aim is to incorporate the financial flow as the cash flow and debts' constraints and labor employment under fuzzy uncertainty. The objectives of the proposed mathematical model are to maximize the increase in cash flow, maximize the total created jobs in the supply chain, and maximize the reliability of consumed raw materials. To encounter the fuzzy uncertainty in this model, a possibilistic programming approach is used. To solve large‐sized problems, the multiobjective simulated annealing algorithm, multiobjective gray wolf optimization, and multiobjective invasive weed optimization are proposed and developed. The numerical results demonstrate that these algorithms solve the problems within about 1% of the required solving time for the augmented ε‐constraint and have similar performance and even better in some cases. The multiobjective simulated annealing algorithm with a weak performance takes less time than the other two algorithms. The multiobjective gray wolf optimization and multiobjective invasive weed optimization algorithms are superior based on the multiobjective performance indices.