Fuzzy multi-objective optimization for multi-site integrated production and distribution planning in two echelon supply chain

Badhotiya, Gaurav Kumar; Soni, Gunjan; Mittal, Murari Lal

doi:10.1007/s00170-018-3204-2

Cited by 30 publications

(13 citation statements)

References 51 publications

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“…4 Procedure of calculating cost of inserting/removing a specific blood center into/from the route Table 1 shows the required input data of all the parameters used in mathematical model. Due to confidentiality of the exact data and proper representation, the input parameter values are scaled down and considered in a uniformly distributed manner [52]. A comparison of the results obtained from the proposed solution algorithm and the ALNS algorithm is provided in Table 2.…”

Section: Application Of the Proposed Solution Algorithm On The Irpt-ml Problemmentioning

confidence: 99%

A solution algorithm for integrated production-inventory-routing of perishable goods with transshipment and uncertain demand

Лю

Hendalianpour²,

Razmi

et al. 2021

Complex Intell. Syst.

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Supply and distribution management of blood products is a challenging task due to their short lifespan. The problem is even more sophisticated considering uncertain demand for these products. This paper addresses integrated inventory-routing of blood in a supply chain network consisting of a single supplier and a group of blood centers. Transshipment among blood centers is allowed to decrease the cost of excess inventory and shortage of goods. A mathematical model is developed that decides on the optimal quantity of supplied blood, delivery plan, inventory level, and quantity of products transshipped between blood centers with the objective of minimizing total costs. In addition, a robust optimization approach is adopted to deal with uncertainty in demand. Since the proposed model is NP-hard, a heuristic solution algorithm is developed that improves solution quality by determining the most efficient change in vehicle routes in each search stage. The efficiency of the proposed algorithm is examined in a set of numerical experiments and using data from a real case of supply and distribution management of blood platelets. The results indicated that allowing transshipment reduces the need for supply capacity at the supplier, product shortage, inventory level, and the total cost.

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Section: Application Of the Proposed Solution Algorithm On The Irpt-ml Problemmentioning

confidence: 99%

A solution algorithm for integrated production-inventory-routing of perishable goods with transshipment and uncertain demand

Лю

Hendalianpour²,

Razmi

et al. 2021

Complex Intell. Syst.

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show abstract

“…Next, the current model does not consider the back-ordering and shortage costs which are also prevalent in realworld applications. Therefore, the consideration of these aspects will strengthen the efficacy and utility of the model (Badhotiya et al, 2018;Badhotiya et al, 2019). Future work can also extend the number of echelons in a supply chain beyond the two-echelon problem as was the case in this study.…”

Section: Conclusion and Future Researchmentioning

confidence: 79%

Modeling Multi-Plant Capacitated Lot Sizing Problem with Interplant Transfer

Patil¹,

Badhotiya²,

Nepal³

et al. 2021

Int J Math, Eng, Manag Sci

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Lot sizing models involve operational and tactical decisions. These decisions may entail multi-level production processes such as assembly operations with multiple plants and limited capacities. Lot sizing problems are widely recognized as NP-hard problems therefore difficult to solve, especially the ones with multiple plants and capacity constraints. The level of complexity rises to an even higher level when there is an interplant transfer between the plants. This paper presents a Genetic Algorithm (GA) based solution methodology applied to large scale multi-plant capacitated lot sizing problem with interplant transfer (MPCLSP-IT). Although the GA has been a very effective and widely accepted meta-heuristic approach used to solve large scale complex problems, it has not been employed for MPCLSP-IT problem. This paper solves the MPCLSP-IT problem in large scale instances by using a genetic algorithm, and in doing so successfully obtains a better solution in terms of computation time when compared to the results obtained by the other methods such as Lagrangian relaxation, greedy randomized adaptive search procedure (GRASP) heuristics, and GRASP-path relinking techniques used in extant literature.

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“…Nevertheless, real-world problems often require the consideration of multiple conflicting criteria to measure the customer service level and supply chain responsiveness. e multiobjective optimisation mainly includes goals related to maximising products' quality level [10], minimising the average tardiness, and minimising delivery [11]. Owing to the difficulties of collecting data and knowledge about production and distribution planning problems, some works examined real-life case studies such as automotive manufacturing [11], apparel industry [12], pharmaceutical industry [13], and plastic industry [14].…”

Section: Related Literaturementioning

confidence: 99%

“…In fact, the backorder level at the last period should be zero under the scenario s in order to avoid lost sales. Equation (11) states that the shipping amount of products from the plant for its successor is received in the next period. e limit of backorder's rate is illustrated in constraint (12).…”

Section: Iptmentioning

confidence: 99%

An Integrated Production-Distribution Planning Problem under Demand and Production Capacity Uncertainties: New Formulation and Case Study

Abid

Ayadi

Masmoudi

2020

Mathematical Problems in Engineering

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In this study, we propose to solve a biobjective tactical integrated production-distribution planning problem for a multisite, multiperiod, multiproduct, sea-air intermodal supply chain network under uncertainties. Two random parameters are considered simultaneously: product replenishment orders and production capacity, which are modelled via a finite set of scenarios, using a two-stage stochastic approach. A corresponding mathematical model is developed, coded, and solved using the LINGO 18.0 software optimisation tool. This model aims to simultaneously minimise the total costs of production in both regular and overtime, inventory, distribution, and backordering activities and maximise the customer satisfaction level over the tactical planning horizon. The AUGMECON technique is applied to handle with the multiobjective optimisation. The applicability and the performance of the proposed model are tested through a real-life case study inspired from a medium-sized Tunisian textile and apparel company. Sensitivity analysis on stochastic parameters and managerial insights for the studied supply chain network are argued based on the empirical findings.

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Fuzzy multi-objective optimization for multi-site integrated production and distribution planning in two echelon supply chain

Cited by 30 publications

References 51 publications

A solution algorithm for integrated production-inventory-routing of perishable goods with transshipment and uncertain demand

A solution algorithm for integrated production-inventory-routing of perishable goods with transshipment and uncertain demand

Modeling Multi-Plant Capacitated Lot Sizing Problem with Interplant Transfer

An Integrated Production-Distribution Planning Problem under Demand and Production Capacity Uncertainties: New Formulation and Case Study

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