Evolutionary multi-objective optimization for the vendor-managed inventory routing problem

2011 ◽  
Author(s):  
Regina M. Azuma ◽  
Guilherme P. Coelho ◽  
Fernando J. Von Zuben
Keyword(s):  
Inventory Routing ◽  
Multi Objective Optimization ◽  
Vendor Managed Inventory ◽  
Routing Problem ◽  
Inventory Routing Problem ◽  
Multi Objective

scholarly journals Inventory Routing Problem in Supply Chain of Perishable Products under Cost Uncertainty

Mathematics ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 382 ◽  
Author(s):  
Muhammad Imran ◽  
Muhammad Salman Habib ◽  
Amjad Hussain ◽  
Naveed Ahmed ◽  
Abdulrahman M. Al-Ahmari
Keyword(s):  
Mathematical Model ◽  
Supply Chain ◽  
Surgical Instruments ◽  
Perishable Products ◽  
Inventory Routing ◽  
Routing Problem ◽  
Cost Uncertainty ◽  
Priority Index ◽  
Inventory Routing Problem ◽  
Multi Objective

This paper presents a multi-objective, multi-period inventory routing problem in the supply chain of perishable products under uncertain costs. In addition to traditional objectives of cost and greenhouse gas (GHG) emission minimization, a novel objective of priority index maximization has been introduced in the model. The priority index quantifies the qualitative social aspects, such as coordination, trust, behavior, and long-term relationships among the stakeholders. In a multi-echelon supply chain, the performance of distributor/retailer is affected by the performance of supplier/distributor. The priority index measures the relative performance index of each player within the supply chain. The maximization of priority index ensures the achievement of social sustainability in the supply chain. Moreover, to model cost uncertainty, a time series integrated regression fuzzy method is developed. This research comprises of three phases. In the first phase, a mixed-integer multi-objective mathematical model while considering the cost uncertainty has been formulated. In order to determine the parameters for priority index objective function, a two-phase fuzzy inference process is used and the rest of the objectives (cost and GHG) have been modeled mathematically. The second phase involves the development of solution methodology. In this phase, to solve the mathematical model, a modified interactive multi-objective fuzzy programming has been employed that incorporates experts’ preferences for objective satisfaction based on their experiences. Finally, in the third phase, a case study of the supply chain of surgical instruments is presented as an example. The results of the case provide optimal flow of products from suppliers to hospitals and the optimal sequence of the visits of different vehicle types that minimize total cost, GHG emissions, and maximizes the priority index.

New Formulation for an Economic Order Quantity Distribution Policy in Vendor-Managed Inventory Routing Problems

2014 ◽  
Vol 945-949 ◽  
pp. 3219-3236 ◽  
Author(s):  
Thiago Guimarães ◽  
Cassius Tadeu Scarpin ◽  
Maria Teresinha Arns Steiner
Keyword(s):  
Planning Horizon ◽  
Transportation Costs ◽  
Economic Order Quantity ◽  
Economic Order ◽  
Inventory Routing ◽  
Order Quantity ◽  
Vendor Managed Inventory ◽  
Routing Problem ◽  
Inventory Routing Problem ◽  
New Formulation

In vendor managed inventory systems, logistics decisions are centralized at the vendor, allowing inventory storage and transportation costs to be reduced simultaneously. Operation of such systems requires the solution of a complex combinatorial optimization problem, known as the Inventory Routing Problem (IRP), which involves managing client inventory and determining the frequency and size of product deliveries as well as the route taken by the vehicle over a given planning horizon. We present a new formulation based on an economic order quantity distribution policy for the multivehicle inventory routing problem (MIRP). A mathematical programming model with additional practical constraints was used for the MIRP. A new heuristic approach that breaks the MIRP down into the following two sub-problems was also proposed: one dealing with the scheduling of deliveries and the formation of delivery clusters over the planning horizon, and the second sub-problem, which builds the routes for the delivery clusters using classic route construction heuristics and a procedure for intra-route improvements. Adjustments between routes are performed with the aid of a new large neighborhood search (LNS) strategy. Small, medium-sized and large scenarios with different storage and transportation costs were generated using parameters based on data from the literature. Extensive computational tests were carried out to determine the effectiveness of the proposed distribution policy and the heuristic used.

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