Optimizing a bi-objective reliable facility location problem with adapted stochastic measures using tuned-parameter multi-objective algorithms

Jalali, Sajjad; Seifbarghy, Mehdi; Sadeghi, Javad; Ahmadi, Samad

doi:10.1016/j.knosys.2015.12.001

Cited by 22 publications

(8 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For instance, Shankar et al . and Jalali et al . reported encouraging results of exploiting a multiobjective particle swarm optimization and multi‐objective bio‐geography‐based algorithm (MOBBO), respectively.…”

Section: Literature Reviewmentioning

confidence: 99%

Financially embedded facility location decisions on designing a supply chain structure: A case study

Nobil

Jalali

Niaki

2018

Systems Engineering

Self Cite

View full text Add to dashboard Cite

The facility location problem and its related financial issues have a significant impact on the configuration of a supply chain structure (SCS). Although affording the setup cost of facilities requires a huge financial investment, a meticulous planning to recoup it has been neglected by an overwhelming majority of previous studies. To fill this gap, an economical investment plan of facilities is determined by striking a trade-off between the contribution of the stockholders' capital and the loan arrangement. In the context of a multiperiod three-echelon SCS, the model also determines the optimum location of plants and the best supply and distribution patterns. The model maximizes the fill rate and the net present value of the periodic cash flow including the sale revenues minus the mortgage payment, purchase, and transportation costs. To solve the proposed problem, a finely tuned Pareto-based nondominated sorting genetic algorithm (NSGA-II) and a multi-objective biogeography-based optimization (MOBBO) algorithm are employed. Moreover, the solution methods are equipped with the heuristic DROP approach and data envelopment analysis to expedite the convergence process and derive the most efficient solutions, respectively.The experiments indicate the adequacy of the solution methods due to the close proximity to the results of the exact branch-and-bound method via the general algebraic modeling system optimization software. Finally, the results of a case study associated with designing SCS of an emerging biofuel industry demonstrate the significant implication of the research. K E Y W O R D Sdata envelopment analysis, facility location problem, multiobjective optimization, net present value, supply chain management 520

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Financially embedded facility location decisions on designing a supply chain structure: A case study

Nobil

Jalali

Niaki

2018

Systems Engineering

Self Cite

View full text Add to dashboard Cite

show abstract

“…Therefore, a multi-objective, multi-mode, multicommodity, and multi-period stochastic model was presented by Najafi et al (2013) that was capable of handling real-life emergency relief conditions under capacity and demand uncertainties. Jalali et al (2016) developed a reliable bi-objective facility location model with provider-side uncertainty of facilities that was employed by a set of scenarios. In addition, a meta-heuristic algorithm called multi-objective biogeography-based optimization was employed to find a near-optimal Pareto solution, which was used to verify the solution that was compared with a non-dominated ranking genetic algorithm (NRGA) and a multi-objective SA algorithm.…”

Section: Introductionmentioning

confidence: 99%

Emergency shelters location-allocation problem concerning uncertainty and limited resources: a multi-objective optimization with a case study in the Central area of Beijing, China

Xü

Qin

et al. 2019

Geomatics, Natural Hazards and Risk

View full text Add to dashboard Cite

The special planning for shelters is an integral part of overall city planning and is a key issue in solving the urban security problem. During the planning of earthquake emergency shelters, it is usually necessary to prioritize the designated shelters to avoid wasting resources. However, in previous research initiatives, special attention has been given to the general location-allocation model without prior consideration of the designated shelters. This article presents a multi-objective supplemental location-allocation optimization model for earthquake emergency shelters that addresses the concerns related to the designated shelters. This model is believed to remain more consistent with the knowledge of shelter planners and policy makers by incorporating the uncertainties of the temporal dynamics of population distribution and the spatially heterogeneous distribution of evacuees under various earthquake damage scenarios by using the elliptical attenuation model for seismic intensity into the location-allocation problem. A case study of the central area of Beijing, China is presented. A modified particle swarm optimization algorithm is applied to solve the supplemental locationallocation model and the general location-allocation model, and the solutions obtained from the two models are then compared. The results show that the solutions of the supplemental location-allocation model significantly minimized the shelter area by prioritizing the designated shelters. By contrast, without prior consideration of the designated shelters, the shelter areas significantly increased, which slightly decreased the evacuation distance in the general location-allocation model. The policy makers could select solutions from the obtained minimal-area scheme using the supplemental location-allocation model that ensures acceptable evacuation distances and minimal budgets by prioritizing the designated shelters during the phased planning of shelters in Beijing. ARTICLE HISTORY

show abstract

“…In modern socioeconomic decision-making systems, people often face decision-making problems with a variety of information conditions, such as stochastic information [1][2][3][4][5][6][7][8][9][10][11][12], 2-dimension uncertain linguistic information [13,14], intuitionistic fuzzy information [15][16][17][18][19], intuitionistic trapezoidal fuzzy information [20,21], triangular fuzzy [22], single-valued neutrosophic numbers [23], trapezoidal fuzzy numbers [24], and neutrosophic hesitant fuzzy information [25]. One of them, which have different time-sequence phases and multiple attribute indexes of normally distributed stochastic variables, like street traffic flow, shopping centre popularity at different times, or the customer waiting times, is referred to as dynamic stochastic multiattribute decisionmaking problems.…”

Section: Introductionmentioning

confidence: 99%

“…Compared with dynamic stochastic decision-making, which comprehensively considers multiple time-sequences, stochastic multi-attribute decision-making under a single static time-sequence environment has received more widespread attention from scholars. These research findings mainly expand on information aggregation operators [1], stochastic dominance [2,3], stochastic multiattribute analysis [4][5][6], set pair connection number analysis [7], prospect stochastic dominance [8,9], bivariate expectation in decision-making [10], probability weighted means [3], and possibility degree interval-valued numbers [11,12], of attribute weights in an unknown state-further expanding the research boundary of stochastic multiattribute decisionmaking. It is difficult for most decision-making results to be comprehensive and rationally optimised based on single time-sequence nodes.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Stochastic Multiattribute Decision‐Making That Considers Stochastic Variable Variance Characteristics under Time‐Sequence Contingency Environments

Huang

2017

Mathematical Problems in Engineering

View full text Add to dashboard Cite

This paper presents a dynamic stochastic decision-making method that considers the characteristics of stochastic variable variances under time-sequence contingency environments for solving stochastic decision-making problems with information from different periods and of indeterminate attribute weights. First, time-sequence weights are obtained using the technique for order preference by similarity to ideal solution (TOPSIS), corresponding with the idea of “stressing the present rather than the past.” After determining the time degree and fully considering the characteristics of normally distributed stochastic variable variances, the attribute weight is determined based on vertical projection distance. Decision-making information is then assembled from two dimensions of time-sequence and attributes, based on the two categories of weighted arithmetic averaging operators of normally distributed stochastic variables, resulting in comprehensive dynamic decision-making from single solution dimensions and a priority sequence of solutions per the order relation criteria of normally distributed stochastic variables. Finally, the validity and practicability of the methods proposed in this paper are verified using an example numerical analysis.

show abstract

Optimizing a bi-objective reliable facility location problem with adapted stochastic measures using tuned-parameter multi-objective algorithms

Cited by 22 publications

References 31 publications

Financially embedded facility location decisions on designing a supply chain structure: A case study

Financially embedded facility location decisions on designing a supply chain structure: A case study

Emergency shelters location-allocation problem concerning uncertainty and limited resources: a multi-objective optimization with a case study in the Central area of Beijing, China

Dynamic Stochastic Multiattribute Decision‐Making That Considers Stochastic Variable Variance Characteristics under Time‐Sequence Contingency Environments

Contact Info

Product

Resources

About