Optimal Deliveries in a Vendor Managed Inventory Service

2014 ◽  
Vol 5 (1) ◽  
pp. 1-14 ◽  
Author(s):  
Chiara Bersani ◽  
Roberto Sacile
Keyword(s):  
Decision Making ◽  
Programming Approach ◽  
Vendor Managed Inventory ◽  
Logistics System ◽  
Service Stations ◽  
Central Production ◽  
Mathematical Programming Approach ◽  
Direct Delivery ◽  
Vehicle Capacity ◽  
Unsatisfied Demand

In a VMI service, a central production center (i.e. the vendor) can control the inventory of each retailer according to the optimization of the costs due both to the overfilling/stock-out of the inventories and to the travels required for the deliveries. In this work, an original mathematical programming approach has been formulated and implemented in order to show that under specific but common conditions (the most important of which are: capacity of the retailer warehouse higher than the demand and than the vehicle capacity, a maximum of two drops for travel, unsatisfied demand lost and not backlogged), a true direct delivery VMI service may not be the best solution. Results are shown on a fictional network as well as on a real logistics system represented by a central depot and by a network of petrol service stations, giving evidence to some peculiar aspects of the VMI service which can be useful on their own to enhance the decision making strategies of a logistic company.

BI-LEVEL PROGRAMMING APPROACH TO OPTIMAL STRATEGY FOR VENDOR-MANAGED INVENTORY PROBLEMS UNDER RANDOM DEMAND

2017 ◽  
Vol 59 (2) ◽  
pp. 247-270 ◽  
Author(s):  
YINXUE LI ◽  
ZHONG WAN ◽  
JINGJING LIU
Keyword(s):  
Decision Making ◽  
Mathematical Program ◽  
Random Variable ◽  
Programming Approach ◽  
Vendor Managed Inventory ◽  
Investment Policies ◽  
Managerial Implications ◽  
Smooth Optimization ◽  
Upper Level ◽  
Shortage Costs

We present an extension of vendor-managed inventory (VMI) problems by considering advertising and pricing policies. Unlike the results available in the literature, the demand is supposed to depend on the retail price and advertising investment policies of the manufacturer and retailers, and is a random variable. Thus, the constructed optimization model for VMI supply chain management is a stochastic bi-level programming problem, where the manufacturer is the upper level decision-maker and the retailers are the lower-level ones. By the expectation method, we first convert the stochastic model into a deterministic mathematical program with complementarity constraints (MPCC). Then, using the partially smoothing technique, the MPCC is transformed into a series of standard smooth optimization subproblems. An algorithm based on gradient information is developed to solve the original model. A sensitivity analysis has been employed to reveal the managerial implications of the constructed model and algorithm: (1) the market parameters of the model generate significant effects on the decision-making of the manufacturer and the retailers, (2) in the VMI mode, much attention should be paid to the holding and shortage costs in the decision-making.

scholarly journals Fuzzy linear mathematical programming in agriculture

BIBECHANA ◽  
2015 ◽  
Vol 13 ◽  
pp. 72-76
Author(s):  
MA Lone ◽  
MS Puktha ◽  
SA Mir
Keyword(s):  
Decision Making ◽  
Mathematical Programming ◽  
Agricultural Land ◽  
Optimal Allocation ◽  
Optimal Solution ◽  
Fuzzy Linear Programming ◽  
Programming Approach ◽  
Fuzzy Mathematical Programming ◽  
Mathematical Programming Approach ◽  
Land Cultivation

In this paper we present a Fuzzy linear Mathematical programming approach for optimal allocation of land under cultivation. Fuzzy Mathematical programming approach is more realistic and flexible optimal solution for the agricultural land cultivation problem. In this study we have discussed how to deal with decision making problems that are described by Fuzzy linear programming (Flp) models and formulated with the elements of uncertainty. This form of approximation can be convenient and sufficient for making good decisions. BIBECHANA 13 (2016) 72-76

A mathematical optimization framework for expansion draft decision making and analysis

2019 ◽  
Vol 15 (1) ◽  
pp. 27-40
Author(s):  
Kyle E. C. Booth ◽  
Timothy C. Y. Chan ◽  
Yusuf Shalaby
Keyword(s):  
Decision Making ◽  
Mathematical Optimization ◽  
Programming Approach ◽  
Optimization Approach ◽  
Optimization Framework ◽  
Selection Decisions ◽  
Mathematical Programming Approach ◽  
Sports Leagues ◽  
Salary Cap ◽  
The Impact

Abstract In this paper, we present and analyze a mathematical programming approach to expansion draft optimization in the context of the 2017 NHL expansion draft involving the Vegas Golden Knights, noting that this approach can be generalized to future NHL expansions and to those in other sports leagues. In particular, we present a novel mathematical optimization approach, consisting of two models, to optimize expansion draft protection and selection decisions made by the various teams. We use this approach to investigate a number of expansion draft scenarios, including the impact of “collaboration” between existing teams, the trade-off between team performance and salary cap flexibility, as well as opportunities for Vegas to take advantage of side agreements in a “leverage” experiment. Finally, we compare the output of our approach to what actually happened in the expansion draft, noting both similarities and discrepancies between our solutions and the actual outcomes. Overall, we believe our framework serves as a promising foundation for future expansion draft research and decision-making in hockey and in other sports.

Simultaneous Modelling-to-Generate-Alternatives Procedure Employing the Firefly Algorithm

2019 ◽  
pp. 19-33
Author(s):  
Julian Scott Yeomans
Keyword(s):  
Decision Making ◽  
Firefly Algorithm ◽  
Objective Measures ◽  
Programming Approach ◽  
Benchmark Problem ◽  
Performance Requirements ◽  
Decision Variables ◽  
Performance Specifications ◽  
Mathematical Programming Approach ◽  
Design Requirements

“Real-world” decision-making applications generally contain multifaceted performance requirements riddled with incongruent performance specifications. This is because decision making typically involves complex problems that are riddled with incompatible performance objectives and contain competing design requirements which are very difficult—if not impossible—to capture and quantify at the time that the supporting decision models are actually constructed. There are invariably unmodelled elements, not apparent during model construction, which can greatly impact the acceptability of the model's solutions. Consequently, it is preferable to generate several distinct alternatives that provide multiple disparate perspectives to the problem. These alternatives should possess near-optimal objective measures with respect to all known objective(s), but be maximally different from each other in terms of their decision variables. This maximally different solution creation approach is referred to as modelling-to-generate-alternatives (MGA). This chapter provides an efficient optimization algorithm that simultaneously generates multiple, maximally different alternatives by employing the metaheuristic firefly algorithm. The efficacy of this mathematical programming approach is demonstrated on a commonly tested engineering optimization benchmark problem.

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