scholarly journals Stochastic Dynamic Programming Model for the Quality Deterioration of an Unreliable Manufacturer

2016 ◽  
Vol 4 (7) ◽  
Author(s):  
Héctor Rivera-Gómez ◽  
Oscar Montaño-Arango ◽  
José Ramón Corona-Armenta ◽  
Eva Selene Hernández-Gress
Keyword(s):  
Optimal Control ◽  
Programming Model ◽  
Control Policy ◽  
Joint Production ◽  
Maintenance Cost ◽  
Joint Control ◽  
Stochastic Dynamic ◽  
Part Type ◽  
Quality Deterioration ◽  
Random Failures

This paper proposes a new integrated model, which analyses deteriorations issues in the optimal control of an unreliable production system. The system under concern was composed of a machine subject to random failures, and repairs, which produced a part type with constant demand. Furthermore, the machine was subject to progressive deterioration reflected mainly in an increasing rate of defectives. Major maintenance such as overhaul was available as a countermeasure to mitigate the effect of the quality deterioration. Given the importance of inventory, backlog and maintenance cost, the main objective of the paper was to develop a control policy, which considered quality deterioration and states the joint production and overhaul strategies to minimize the total incurred cost. The structure of the joint control policy was defined through numerical techniques. The obtained results showed the strong influence of deterioration issues in the optimal control policy.  

scholarly journals Coordinating Pricing and Empty Container Repositioning in Two-Depot Shipping Systems

2020 ◽  
Vol 54 (6) ◽  
pp. 1697-1713
Author(s):  
Tao Lu ◽  
Chung-Yee Lee ◽  
Loo-Hay Lee
Keyword(s):  
Programming Model ◽  
Control Policy ◽  
Approximate Model ◽  
Dynamic Program ◽  
Stochastic Dynamic ◽  
Empty Container ◽  
Empty Container Repositioning ◽  
Joint Decisions ◽  
Dimensional State Space ◽  
In Transit

This paper studies joint decisions on pricing and empty container repositioning in two-depot shipping services with stochastic shipping demand. We formulate the problem as a stochastic dynamic programming model. The exact dynamic program may have a high-dimensional state space because of the in-transit containers. To cope with the curse of dimensionality, we develop an approximate model where the number of in-transit containers on each vessel is approximated with a fixed container flow predetermined by solving a static version of the problem. Moreover, we show that the approximate value function is [Formula: see text]-concave, thereby characterizing the structure of the optimal control policy for the approximate model. With the upper bound obtained by solving the information relaxation–based dual of the exact dynamic program, we numerically show that the control policies generated from our approximate model are close to optimal when transit times span multiple periods.

scholarly journals Integrating Dynamic Pricing and Inventory Control for Fresh Agriproduct under Multinomial Logit Choice

2018 ◽  
Vol 2018 ◽  
pp. 1-12
Author(s):  
Xiong-zhi Wang ◽  
Wenliang Zhou
Keyword(s):  
Dynamic Pricing ◽  
Programming Model ◽  
Choice Model ◽  
Multinomial Logit ◽  
Linear Ordering ◽  
Stochastic Dynamic ◽  
Inventory Problem ◽  
Numerical Illustration ◽  
Dynamic Stochastic ◽  
Joint Pricing

In this article, we investigate a joint pricing and inventory problem for a retailer selling fresh agriproducts (FAPs) with two-period shelf lifetime in a dynamic stochastic setting, where new and old FAPs are on sale simultaneously. At the beginning of each period the retailer makes ordering decision for new FAP and sets regular and discount price for new and old inventories, respectively. After demand realization, the expired leftover is disposed and unexpired inventory is carried to the next period, continuing selling. Unmet demand of all FAPs is backordered. The objective is to maximize the total expected discount profit over the whole planning horizon. We present a price-dependent, stochastic dynamic programming model taking into account zero lead time, linear ordering costs, inventory holding, and backlogging costs, as well as disposal cost. Considering the influence of the perishability, we integrate a Multinomial Logit (MNL) choice model to describe the consumer behavior on purchasing fresh or nonfresh product. By way of the inverse of the price vector, the original formulation can be transferred to be jointly concave and tractable. Finally we characterize the optimal policy and develop effective methods to solve the problem and conduct a simple numerical illustration.

scholarly journals A Bilevel Stochastic Dynamic Programming Model to Assess the Value of Information on Actual Food Quality at Wholesale Markets

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Xiangyu Hou ◽  
Rene Haijema ◽  
Dacheng Liu
Keyword(s):  
Dynamic Programming ◽  
Programming Model ◽  
Market Price ◽  
Programming Approach ◽  
Stochastic Dynamic ◽  
Wholesale Market ◽  
Dynamic Programming Approach ◽  
Additional Information ◽  
Similar Appearance ◽  
Inventory Decision

In the fresh produce wholesale market, the market price is determined by the total demand and supply. The price is stochastic, and either wholesaler or retailer has few influence on it. In the wholesaler’s inventory decision, the price’s uncertainty plays an important role as well as the uncertainty from the demand side: the wholesaler makes his decision based on the retailer’s ordering, which is influenced by the stochastic market price and the distribution of the consumer’s demand. In addition, when at the wholesale stage, the products show a similar quality of similar appearance. With more efforts being input, the wholesaler could detect and record more additional information than that reflected from the appearance. Based on this, he can classify the quality into different levels. No experience shows how the wholesaler could use the underlying quality information and how much this information could improve his profit. To describe and explore this problem, a bilevel dynamic programming approach is employed. We evaluate different strategies of using the underlying information, show the features of the optimal policy, develop heuristics, and discuss the influence of factors such as quality and market price. We also develop the managerial principles for the practical use.

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