Redesign of a supply network by considering stochastic demand

Paz, Juan Camilo; Orozco, Juan Andrés Ramirez; Salinas, José Luis Rangel; Clavijo-Buriticá, Nicolás; Escobar, John Willmer

doi:10.5267/j.ijiec.2015.5.001

Cited by 10 publications

(9 citation statements)

References 15 publications

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“…It is also important to point that techniques such as Sample Average Approximation (SAA) are very useful to find solutions with stochastic variables (Santoso et al, 2005;Escobar, 2012;Escobar et al, 2012;Escobar, 2009;Escobar et al, 2013;Paz et al, 2015;Mafla & Escobar, 2015).…”

Section: Methodologies For the Solution Of Lp Problemsmentioning

confidence: 99%

“…The proposed SAA scheme used on this paper is based on the work proposed by Kleywegt et al (2002) and Paz et al (2015). First, a limited sample of the lot sizing problem and configurations of the mix of products is generated; in which each of these decisions is determined from multiple random generation by using random demand scenarios and corresponding stochastic model solutions.…”

Section: Methodologies For the Solution Of Lp Problemsmentioning

confidence: 99%

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A mathematical model for the product mixing and lot-sizing problem by considering stochastic demand

Rodado¹,

Escobar²,

Cáceres³

et al. 2017

10.5267/j.ijiec

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The product-mix planning and the lot size decisions are some of the most fundamental research themes for the operations research community. The fact that markets have become more unpredictable has increaed the importance of these issues, rapidly. Currently, directors need to work with product-mix planning and lot size decision models by introducing stochastic variables related to the demands, lead times, etc. However, some real mathematical models involving stochastic variables are not capable of obtaining good solutions within short commuting times. Several heuristics and metaheuristics have been developed to deal with lot decisions problems, in order to obtain high quality results within short commuting times. Nevertheless, the search for an efficient model by considering product mix and deal size with stochastic demand is a prominent research area. This paper aims to develop a general model for the product-mix, and lot size decision within a stochastic demand environment, by introducing the Economic Value Added (EVA) as the objective function of a product portfolio selection. The proposed stochastic model has been solved by using a Sample Average Approximation (SAA) scheme. The proposed model obtains high quality results within acceptable computing times.

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Section: Methodologies For the Solution Of Lp Problemsmentioning

confidence: 99%

Section: Methodologies For the Solution Of Lp Problemsmentioning

confidence: 99%

A mathematical model for the product mixing and lot-sizing problem by considering stochastic demand

Rodado¹,

Escobar²,

Cáceres³

et al. 2017

10.5267/j.ijiec

Self Cite

View full text Add to dashboard Cite

show abstract

“…Unsupervised learning is a striking line to continue the development of this study and thus improve the quality of the optimization model inputs. Besides, developing a stochastic solution of the proposed model using Sample Average Approximation (SAA) (Escobar et al 2013b ; Mafia and Escobar, 2015 ; Paz et al 2015 ; Rodado et al 2017 , Vélez et al 2021 ) would be considered. Finally, heuristic or metaheuristic based on granular search (Escobar and Linfati 2012 ; Escobar et al 2013a ; Escobar et al 2014a ; Escobar et al 2014b ; Puenayán et al 2014 ; Bernal et al 2017 ; Bernal et al 2018 ; Bernal et al 2021 ; Escobar 2022 and Riaño et al 2022 ) for solving large size instances could be considered.…”

Section: Concluding Remarks and Future Workmentioning

confidence: 99%

Two-stage optimization scheme of routing scheduling from a single distribution center to multiple customers

2023

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This paper proposes a solution scheme for the problem of scheduling deliveries from distribution centers. The solution scheme is developed in two phases. The first phase corresponds to the collection and processing of data, and the second phase includes the design of a customer assignment model based on the data of the first phase. The first phase provides the characteristics of the customers, the number of orders to be dispatched, and the number of visited customers. In the second phase, optimization techniques are used, considering scheduling and assignment problem-solving methodologies. The optimization model provides the correct assignment of customer orders to vehicles, reducing the number of trips and the fleet's size and occupation percentage. The proposed solution scheme is developed with a company operating a distribution center responsible for dispatching vehicles to all retail customers, wholesalers, and distributors in the Province of Concepción, Province of Arauco, and Province of Itata in Chile. The obtained results are prominent and show that the proposed methodology can be extended to companies performing storage, transportation, and distribution tasks with similar characteristics.

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“…Future research studies are related to the solution of the stochastic version of the problem by using techniques such as Sample Average Approximation (SAA) (Paz et al [44], Rodado et al [45] and Escobar et al [46]), and also the use of heuristic or metaheuristic strategies based on a granular search for large instances such as similar works proposed by Bernal et al [47], Bernal et al [48], Bernal et al [49], Escobar [50], Puenayan et al [51], and Escobar et al [52], Escobar and Linfati [53], and Linfati et al [54].…”

Section: Conflicts Of Interestmentioning

confidence: 99%

A Mathematical Model for Scheduling and Assignment of Customers in Hospital Waste Collection Routes

2021

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The collection, transport, and final disposal of hospital waste may cause contamination and disease if improperly handled. Therefore, such residues are hazardous to the health of waste collectors. These wastes are generated by public agencies, such as hospitals, family health centers, dialysis centers, and private healthcare providers. In this study, a mixed-integer linear programming model is proposed for monthly customer scheduling and route assignment. The proposed approach was fulfilled according to customers’ collection frequency, truck capacity, and customer geographical location. The proposed mathematical model successfully balanced the number of customers and the workload during each day. The effectiveness of the proposed model was tested on data obtained from a waste collection company. The model has been implemented in AMPL language, and the performance of commercial solvers, GUROBI and CPLEX, to obtain an optimal solution were tested. The results show the efficiency of the proposed approach to balance the workload concerning previous scheduling is done ad hoc at the company. The use of the formulated model provides an automatic procedure that was previously performed manually. The methodology can be adapted to other companies with similar requirements.

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Redesign of a supply network by considering stochastic demand

Cited by 10 publications

References 15 publications

A mathematical model for the product mixing and lot-sizing problem by considering stochastic demand

A mathematical model for the product mixing and lot-sizing problem by considering stochastic demand

Two-stage optimization scheme of routing scheduling from a single distribution center to multiple customers

A Mathematical Model for Scheduling and Assignment of Customers in Hospital Waste Collection Routes

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