A simulation approach for multi-stage supply chain optimization to analyze real world transportation effects

Peirleitner, Andreas J.; Altendorfer, Klaus; Felberbauer, Thomas

doi:10.1109/wsc.2016.7822268

Cited by 11 publications

(10 citation statements)

References 11 publications

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“…Nikolopoulou and Ierapetritou (2012) coupled MILP and agent-based simulation to minimize total cost of a SC network by recognising the optimal operational decisions such as production planning decisions. Peirleitner, Altendorfer, and Felberbauer (2016) integrated a Non-dominated Sorting Genetic Algorithm (NSGA-II) and an Agent-based simulation model to recognise optimal inventory policies for supply chain members under demand and replenishment lead time uncertainties. The objective of the model was to determine an optimal reorder point and order quantity of Stock Keeping Units (SKUs) for entities in a general supply chain network so as to minimize the total supply chain cost, while maximizing service level for retailers.…”

Section: Simulation-based Optimization (Sbo) For the Supply Chainmentioning

confidence: 99%

Using simulation-based system dynamics and genetic algorithms to reduce the cash flow bullwhip in the supply chain

Badakhshan

Humphreys

Maguire

et al. 2020

International Journal of Production Research

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Section: Simulation-based Optimization (Sbo) For the Supply Chainmentioning

confidence: 99%

Using simulation-based system dynamics and genetic algorithms to reduce the cash flow bullwhip in the supply chain

Badakhshan

Humphreys

Maguire

et al. 2020

International Journal of Production Research

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“…Among modern papers, metaheuristic in general and genetic algorithms in particular are distinguished. For instance, Peirleitner et al considered a stochastic supply chain management problem [10]. The problem is stated as biobjective optimisation problem.…”

Section: Related Work and Noveltymentioning

confidence: 99%

Simulation-Optimisation Approach to Stochastic Inventory Control with Perishability

Jackson

2019

ITMS

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In order to tailor inventory control to urgent needs of grocery retail, the discrete-event simulation model with realistic perishability mechanics is proposed in the paper. The model is stochastic and operates with multiple products under constrained total inventory capacity. Besides, the model under consideration is distinguished by quantity discount, uncertain replenishment lags and lost sales. The paper presents both mathematical description and algorithmic implementation. An optimisation framework based on a genetic algorithm is also proposed for deriving an optimal control policy. The proposed approach contributes to the field of industrial engineering by providing a simple and flexible way to compute nearly-optimal inventory control parameters.

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“…The customer demand is given by the following matrix (for each product p, and each period t), see tables below: The different parameters and input data are uniformly generated in the intervals as shown below. The inventory costs at all levels [3,6] The procurement costs from DC to wholesalers [5,16] The procurement costs from manufacturer to DC [7,18] The procurement costs from supplier to manufacturer [100,250] The transportation costs from wholesaler to customer [10,30] The transportation costs from DCs to wholesaler [20,40] The transportation costs from plants to DCs [30,50], The storage capacities of DCs, manufacturers and suppliers Generated according to demands at each level. The production cost at plants [5,16] The setup time [1,30] The setup cost According to setup time The consumption rate of product Rate [10%, 60%] of capacity Rate of raw material in product [0,20] The proposed solution methodology gave the following results as tabulated below: As we can see in those tables, the customer demands are aggregated at wholesaler's level.…”

Section: Experimentationmentioning

confidence: 99%

On The Multi-Stage Optimization In Supply Chain (A Case Study of WEMY Industries Limited)

Oyekan¹,

Ewebajo²,

Temisan³

2019

AIMS Research Journal Publication

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This work proposes a new mathematical model based on a multi-objective solution for the optimization problem in supply chain. The analyzed supply chain includes the suppliers, manufacturers, distribution centers and wholesalers, considering that orders come from customers to wholesalers. A mathematical model that solves the optimization problem regarding durations and costs in the execution of customer orders is proposed. Many constraints are supported in this mathematical model such as suppliers and manufacturers capacities and balanced stocks. The aim of the proposed method is to find a near-optimal solution that minimizes criteria of cost and time along the whole supply chain process. The mathematical model was tested on WEMY Industries Limited supply chain, the results obtained by executing the heuristics approach and the mathematical model shows that it is more efficient than the heuristic. The GAP 15.02 % gotten from the proposed model improves the actual solution, this will assist in reducing costs of production and transportation of the goods and profit will be maximized as the customer of physical distribution and logistics emerged in the 1960s and 1970s. In the 1990s, a new name emerges: "Supply demands are meant genuinely respecting the delivery date of the customer.

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A simulation approach for multi-stage supply chain optimization to analyze real world transportation effects

Cited by 11 publications

References 11 publications

Using simulation-based system dynamics and genetic algorithms to reduce the cash flow bullwhip in the supply chain

Using simulation-based system dynamics and genetic algorithms to reduce the cash flow bullwhip in the supply chain

Simulation-Optimisation Approach to Stochastic Inventory Control with Perishability

On The Multi-Stage Optimization In Supply Chain (A Case Study of WEMY Industries Limited)

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