A Stochastic Optimization Model for a Joint Pricing and Resource Allocation Problem

2020 ◽  
Author(s):  
Qiunan Meng ◽  
Xun Xu
Keyword(s):  
Resource Allocation ◽  
Ant Colony Algorithm ◽  
Deterministic Model ◽  
Programming Model ◽  
Stochastic Approach ◽  
Mixed Integer ◽  
Two Stage ◽  
Joint Decision ◽  
Joint Problem ◽  
Joint Pricing

Abstract In a competitive and volatile market, the price is needed to make in consideration of the uncertain demands of the customers and the limited capacities of enterprises. This requires the coordination decisions on pricing, delivery and resource allocation to increase profit and guarantee service quality for firms. The joint decision on pricing and resource allocation with demand and processing time uncertainty is becoming an issue for a profit-maximizing firm that produces various products. We propose a two-stage model based on stochastic programming to address this joint problem, aiming to maximize profit of products. We present a scenario-simulation approach to describe the stochastic variables; then the deterministic two-stage mixed integer linear programming model is formulated depending on those scenarios. We develop an algorithm by ant colony algorithm to obtain the near-optimal solutions of the models above. The numerical experiments were conducted to validate the proposed models. The results show that the stochastic approach outperforms the deterministic model in the different problem scales and yield the better values of compared metrics. The outcomes also imply that this joint pricing model can provide managerial inspiration for enterprises in the customization environment.

scholarly journals A two-stage stochastic optimization model for the retail multiskilled personnel scheduling problem: a k-chaining policy with $k \ge 2$

2021 ◽  
Vol 19 (1) ◽  
pp. 892-917
Author(s):  
Yessica Andrea Mercado ◽  
◽  
César Augusto Henao ◽  
Virginia I. González
Keyword(s):  
Stochastic Optimization ◽  
Service Sector ◽  
Deterministic Model ◽  
Programming Model ◽  
Simulated Data ◽  
Working Hours ◽  
Mixed Integer ◽  
Two Stage ◽  
Demand Variability

<abstract> <p>Considering an uncertain demand, this study evaluates the potential benefits of using a multiskilled workforce through a k-chaining policy with $k \ge 2$. For the service sector and, particularly for the retail industry, we initially propose a deterministic mixed-integer linear programming model that determines how many employees should be multiskilled, in which and how many departments they should be trained, and how their weekly working hours will be assigned. Then, the deterministic model is reformulated using a two-stage stochastic optimization (TSSO) model to explicitly incorporate the uncertain personnel demand. The methodology is tested for a case study using real and simulated data derived from a Chilean retail store. We also compare the TSSO approach solutions with the myopic approaches' solutions (i.e., zero and total multiskilling). The case study is oriented to answer two key questions: how much multiskilling to add and how to add it. Results show that TSSO approach solutions always report maximum reliability for all levels of demand variability considered. It was also observed that, for high levels of demand variability, a k-chaining policy with $k \ge 2$ is more cost-effective than a 2-chaining policy. Finally, to evaluate the conservatism level in the solutions reported by the TSSO approach, two truncation types in the probability density function (pdf) associated with the personnel demand were considered. Results show that, if the pdf is only truncated at zero (more conservative truncation) the levels of required multiskilling are higher than when the pdf is truncated at 5th and 95th percentiles (less conservative truncation).</p> </abstract>

Green Profit Maximization Through Joint Pricing and Production Planning of New and Remanufactured Products

2013 ◽  
Author(s):  
Minjung Kwak ◽  
Katherine Koritz ◽  
Harrison M. Kim
Keyword(s):  
End Of Life ◽  
Production Planning ◽  
Environmental Sustainability ◽  
Programming Model ◽  
Profit Maximization ◽  
Mixed Integer ◽  
Market Demand ◽  
Conflicting Objectives ◽  
Joint Pricing ◽  
Buy Back

To achieve “green profit” in their business, manufacturers who produce both new and remanufactured products must optimize their pricing and production decisions simultaneously. They must determine the buy-back price and take-back quantity of end-of-life products as well as the selling prices and production quantities of new and remanufactured products. With an aim to assist in optimal pricing and production planning, this paper presents a mixed-integer programming model that optimizes the three prices (of buyback, new and remanufactured products) and the corresponding production plan simultaneously. The model considers the two conflicting objectives of maximizing economic profitability and maximizing environmental impact saving. The model helps address potential barriers to remanufacturing, which include limited economic, and/or environmental sustainability of remanufacturing, imbalance between the supply of end-of-life products and the market demand for remanufactured products, and cannibalization of the sales of new products. The developed model is illustrated with an example of engine water pump.

Preemptive Open-Shop Scheduling: Network Flow Based Algorithm

2011 ◽  
Vol 215 ◽  
pp. 111-114 ◽  
Author(s):  
Yong Zhan ◽  
Yu Guang Zhong ◽  
Hai Tao Zhu
Keyword(s):  
Resource Allocation ◽  
Network Flow ◽  
Flow Model ◽  
Programming Model ◽  
Maximum Flow ◽  
Open Shop ◽  
Mixed Integer ◽  
Scheduling Problem ◽  
Open Shop Scheduling ◽  
Shop Scheduling

Preemptive open-shop scheduling problem was studied, and a network flow based algorithm was presented. Firstly, based on the characteristics of the preemptive open-shop, the scheduling problem was formulated as a mixed-integer programming model with the objective to minimize the make-span. The maximum flow model of the preemptive open-shop was developed to model the machine resource allocation and time constraints. Moreover a new preflow push algorithm for the maximum flow model was put forward. Based on the solution of machine resource allocation problem got by preflow push algorithm, the sequences of the tasks processed by each machine were determined by calculating the matrix of the processing times and decrementing set. Finally, the validity of the developed scheduling algorithm is illustrated by randomly generated example.

scholarly journals A joint pricing and network design model for a closed-loop supply chain under disruption (Glass Industry)

2022 ◽  
Author(s):  
Matineh ziari ◽  
Mohsen Sheikh Sajadieh
Keyword(s):  
Supply Chain ◽  
Glass Industry ◽  
Closed Loop ◽  
Resource Constraints ◽  
Programming Model ◽  
Real Data ◽  
Mixed Integer ◽  
Closed Loop Supply Chain ◽  
Closed Loop Supply Chains ◽  
Joint Pricing

Closed-loop supply chains have attracted more attention by researchers and practitioners due to strong government regulations, environmental issues, social responsibilities and natural resource constraints over past few years. This paper presents a mixed-integer linear programming model to design a closed-loop supply chain network and optimizing pricing policies under random disruption. Reusing the returned products is applied as a resilience strategy to cope with the waste of energy and improving supply efficiency. Moreover, it is necessary to find the optimal prices for both final and returned products. Therefore, the model is formulated based on demand function and it maximizes total supply chain’s profit. Finally, its application is explored through using the real data of an industrial company in glass industry.

scholarly journals A Two-Stage Algorithm for the Closed-Loop Location-Inventory Problem Model Considering Returns in E-Commerce

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Yanhui Li ◽  
Mengmeng Lu ◽  
Bailing Liu
Keyword(s):  
Ant Colony Algorithm ◽  
Closed Loop ◽  
Programming Model ◽  
Optimal Solution ◽  
Ant Colony ◽  
Inventory Problem ◽  
Two Stage ◽  
Logistics System ◽  
Problem Model ◽  
The Right

Facility location and inventory control are critical and highly related problems in the design of logistics system for e-commerce. Meanwhile, the return ratio in Internet sales was significantly higher than in the traditional business. Focusing on the existing problem in e-commerce logistics system, we formulate a closed-loop location-inventory problem model considering returned merchandise to minimize the total cost which is produced in both forward and reverse logistics networks. To solve this nonlinear mixed programming model, an effective two-stage heuristic algorithm named LRCAC is designed by combining Lagrangian relaxation with ant colony algorithm (AC). Results of numerical examples show that LRCAC outperforms ant colony algorithm (AC) on optimal solution and computing stability. The proposed model is able to help managers make the right decisions under e-commerce environment.

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