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 Optimization on the Multi-period Empty Container Repositioning Problem in Regional Port Cluster Based upon Inventory Control Strategies

2021 ◽  
Author(s):  
Jiaxin Cai ◽  
Yubo Li ◽  
Yandong Yin ◽  
Xiaohan Wang ◽  
Zhihong Jin
Keyword(s):  
Inventory Control ◽  
Control Strategy ◽  
Programming Model ◽  
Mixed Integer ◽  
Dynamic Programming Method ◽  
Empty Container ◽  
Total Cost ◽  
Shipping Company ◽  
Empty Container Repositioning ◽  
Port Clusters

Abstract Within the area of regional port clusters, this paper establishes a multi-period mixed integer programming model to optimize the empty container repositioning between public hinterlands and ports, comprehensively considering the quantitative and periodic inventory control strategy. By using Markov decision process combined with dynamic programming method, this paper dynamically optimizes the empty container inventory threshold (D;U) under quantitative strategy and S under periodical strategy at each port within the regional port clusters. On this basis, this paper optimizes the empty container repositioning scheme between public hinterlands and ports. Meanwhile, Liaoning coastal regional port cluster and its northeast hinterland are selected as the objects to solve this model and the results show that the total cost of shipping company can be saved by 14.16% and 11.92% respec- tively by the quantitative and periodical inventory control strategy. Selecting the quantity of public hinterland terminals, the empty container demand of public hinterland terminals and ports, the inventory threshold of empty containers and other factors, this paper carries on the sensitivity analysis. This paper validates inventory control strategy can weaken the shipping company in the influence of the external environment changes. And the quantitativeinventory control strategy can reduce the total cost value to a greater extent and more effective in cost control than periodical strategy.

scholarly journals A New Separable Piecewise Linear Learning Algorithm for the Stochastic Empty Container Repositioning Problem

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Shaorui Zhou ◽  
Xiaopo Zhuo ◽  
Zhiming Chen ◽  
Yi Tao
Keyword(s):  
Cost Function ◽  
Programming Model ◽  
Learning Algorithm ◽  
Piecewise Linear ◽  
Two Stage ◽  
Empty Container ◽  
Expected Cost ◽  
Stochastic Programs ◽  
Empty Container Repositioning ◽  
Empty Containers

A common challenge faced by liner operators in practice is to effectively allocate empty containers now in a way that minimizes the expectation of costs and reduces inefficiencies in the future with uncertainty. To incorporate uncertainties in the operational model, we formulate a two-stage stochastic programming model for the stochastic empty container repositioning (ECR) problem. This paper proposes a separable piecewise linear learning algorithm (SPELL) to approximate the expected cost function. The core of SPELL involves learning steps that provide information for updating the expected cost function adaptively through a sequence of piecewise linear separable approximations. Moreover, SPELL can utilize the network structure of the ECR problem and does not require any information about the distribution of the uncertain parameters. For the two-stage stochastic programs, we prove the convergence of SPELL. Computational results show that SPELL performs well in terms of operating costs. When the scale of the problem is very large and the dimensionality of the problem is increased, SPELL continues to provide consistent performance very efficiently and exhibits excellent convergence performance.

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 Optimization of Empty Container Repositioning in Liner Shipping

2020 ◽  
Vol 8 (1) ◽  
pp. 1
Author(s):  
Hüseyin Gençer ◽  
M. Hulusi Demir
Keyword(s):  
Mathematical Programming ◽  
Deterministic Model ◽  
Programming Model ◽  
Real Life ◽  
Real Data ◽  
Mixed Integer ◽  
Computational Time ◽  
Mathematical Programming Model ◽  
Empty Container ◽  
Empty Container Repositioning

Empty container repositioning (ECR), which arises due to imbalances in world trade, causes extra costs for the container liner carrier companies. Therefore, one of the main objectives of all liner carriers is to reduce ECR costs. Since ECR decisions involve too many parameters, constraints and variables, the plans based on real-life experiences cannot be effective and are very costly. For this purpose, this study introduces two mathematical programming models in order to make ECR plans faster, more efficient and at the lowest cost. The first mathematical programming model developed in this study is a mixed-integer linear programming (MILP) model and the second mathematical programming model is a scenario-based stochastic programming (SP) model, which minimize the total ECR costs. Unlike the deterministic model, the SP model takes into account the uncertainty in container demand. Both models have been tested with real data taken from a liner carrier company. The numerical results showed that, in a reasonable computational time, both models provide better results than real-life applications of the liner carrier company.

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.

Application of a mixed fuzzy decision making and optimization programming model to the empty container allocation

2010 ◽  
Vol 10 (4) ◽  
pp. 1071-1079 ◽  
Author(s):  
Chien-Chang Chou ◽  
Rong-Hua Gou ◽  
Chaur-Luh Tsai ◽  
Ming-Cheng Tsou ◽  
Chun-Pong Wong ◽  
...  
Keyword(s):  
Decision Making ◽  
Programming Model ◽  
Fuzzy Decision Making ◽  
Fuzzy Decision ◽  
Empty Container ◽  
Optimization Programming

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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