Insights to optimise marketing decisions on pig-grower farms

2019 ◽  
Vol 59 (6) ◽  
pp. 1126 ◽  
Author(s):  
S. V. Rodríguez-Sanchez ◽  
L. M. Pla-Aragones ◽  
R. De Castro
Keyword(s):  
Weight Control ◽  
Time Window ◽  
Programming Model ◽  
Transportation Cost ◽  
Management Policy ◽  
Mixed Integer ◽  
Factors Affecting ◽  
Pig Production ◽  
Marketing Policy ◽  
Feeding Program

Modern pig production in a vertically integrated company is a highly specialised and industrialised activity, requiring increasingly complex and critical decision-making. The present paper focuses on the decisions made on the pig-grower farms operating on an all-in–all-out management policy at the last stage of pig production. Based on a mixed-integer linear-programming model, an assessment for specific parameters to support marketing decisions on farms without individual weight control is made. The analysis of several key factors affecting the optimal marketing policy, such as transportation cost, when and how many pigs to deliver to the abattoir and weight homogeneity of the batch, served to gain insight into marketing decisions. The results confirmed that not just the feeding program, but also the grading price system, transportation and batch homogeneity have an enormous impact on the optimal marketing policy of fattening farms in a vertically integrated company. In addition, within the range of conditions considered, a time window of 4 weeks was deemed as optimal for delivering animals to the abattoir and the subsequent revenue was 15% higher than with traditional marketing rules.

scholarly journals A Synchronous Optimization Model for Multiship Shuttle Tanker Fleet Design and Scheduling Considering Hard Time Window Constraint

2018 ◽  
Vol 2018 ◽  
pp. 1-8
Author(s):  
Zhenfeng Jiang ◽  
Dongxu Chen ◽  
Zhongzhen Yang
Keyword(s):  
Time Window ◽  
Programming Model ◽  
Transportation Cost ◽  
Exact Algorithm ◽  
Mixed Integer ◽  
Routing Problem ◽  
Hard Time ◽  
Proposed Model ◽  
Total Transportation Cost ◽  
Time Window Constraints

A Synchronous Optimization for Multiship Shuttle Tanker Fleet Design and Scheduling is solved in the context of development of floating production storage and offloading device (FPSO). In this paper, the shuttle tanker fleet scheduling problem is considered as a vehicle routing problem with hard time window constraints. A mixed integer programming model aiming at minimizing total transportation cost is proposed to model this problem. To solve this model, we propose an exact algorithm based on the column generation and perform numerical experiments. The experiment results show that the proposed model and algorithm can effectively solve the problem.

scholarly journals A Generalized Minimum Cost Flow Model for Multiple Emergency Flow Routing

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Jianxun Cui ◽  
Shi An ◽  
Meng Zhao
Keyword(s):  
Flow Model ◽  
Time Window ◽  
Programming Model ◽  
Real Life ◽  
Minimum Cost ◽  
Emergency Evacuation ◽  
Mixed Integer ◽  
Fixed Cost ◽  
Minimum Cost Flow ◽  
Cost Flow

During real-life disasters, that is, earthquakes, floods, terrorist attacks, and other unexpected events, emergency evacuation and rescue are two primary operations that can save the lives and property of the affected population. It is unavoidable that evacuation flow and rescue flow will conflict with each other on the same spatial road network and within the same time window. Therefore, we propose a novel generalized minimum cost flow model to optimize the distribution pattern of these two types of flow on the same network by introducing the conflict cost. The travel time on each link is assumed to be subject to a bureau of public road (BPR) function rather than a fixed cost. Additionally, we integrate contraflow operations into this model to redesign the network shared by those two types of flow. A nonconvex mixed-integer nonlinear programming model with bilinear, fractional, and power components is constructed, and GAMS/BARON is used to solve this programming model. A case study is conducted in the downtown area of Harbin city in China to verify the efficiency of proposed model, and several helpful findings and managerial insights are also presented.

scholarly journals Collaborative Multidepot Petrol Station Replenishment Problem with Multicompartments and Time Window Assignment

2020 ◽  
Vol 2020 ◽  
pp. 1-22
Author(s):  
Guangcan Xu ◽  
Maozeng Xu ◽  
Yong Wang ◽  
Yong Liu ◽  
Qiguang Lv
Keyword(s):  
Energy Supply ◽  
Time Window ◽  
Programming Model ◽  
Urban Transportation ◽  
Mixed Integer ◽  
Supply Network ◽  
Transportation Energy ◽  
The Sustainable Development ◽  
Petrol Station ◽  
Chongqing City

Energy supply is an important system that affects the overall efficiency of urban transportation. To improve the system operational efficiency and reduce costs, we formulate and solve a collaborative multidepot petrol station replenishment problem with multicompartments and time window assignment by establishing a mixed-integer linear programming model. The hybrid heuristic algorithm composed of genetic algorithm and particle swarm optimization is used as a solution, and then the Shapley value method is applied to analyze the profit allocation of each petrol depot under different coalitions. The optimal membership sequence of the cooperation is determined according to the strict monotone path. To analyze and verify the effectiveness of the proposed method, a regional petrol supply network in Chongqing city in China is investigated. Through cooperation between petrol depots in the supply network, the utilization of customer clustering, time window coordination, and distribution truck sharing can significantly reduce the total operation costs and improve the efficiency of urban transportation energy supply. This approach can provide theoretical support for relevant government departments and enterprises to make optimal decisions. The implementation of the joint distribution of energy can promote the sustainable development of urban transportation.

scholarly journals A Path-Based Selection Solution Approach for the Low Carbon Vehicle Routing Problem with a Time-Window Constraint

2020 ◽  
Vol 10 (4) ◽  
pp. 1489 ◽  
Author(s):  
Xianlong Ge ◽  
Xiaobo Ge ◽  
Weixin Wang
Keyword(s):  
Time Window ◽  
Programming Model ◽  
Model Simulation ◽  
Path Selection ◽  
Urban Traffic ◽  
Mixed Integer ◽  
Low Carbon ◽  
Gradual Improvement ◽  
Solution Approach ◽  
Urban Distribution

Due to the gradual improvement of urban traffic network construction and the increasing number of optional paths between any two points, how to optimize a vehicle travel path in a multi-path road network and then improve the efficiency of urban distribution has become a difficult problem for logistics companies. For this purpose, a mixed-integer mathematical programming model with a time window based on multiple paths for urban distribution in a multi-path environment is established and its exact solution solved using software CPLEX. Additionally, in order to test the application and feasibility of the model, simulation experiments were performed on the four parameters of time, distance, cost, and fuel consumption. Furthermore, using Jingdong (JD), the main urban area in Chongqing, as an example, the experimental results reveal that an algorithm that considers the path selection can significantly improve the efficiency of urban distribution in metropolitan areas with complex road structures.

scholarly journals Five-Echelon Multiobjective Health Services Supply Chain Modeling under Disruption

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Farnaz Javadi Gargari ◽  
Mahjoube Sayad ◽  
Seyed Ali Posht Mashhadi ◽  
Abdolhossein Sadrnia ◽  
Arman Nedjati ◽  
...  
Keyword(s):  
Supply Chain ◽  
Optimization Model ◽  
Optimization Problem ◽  
Programming Model ◽  
Transportation Cost ◽  
Real Data ◽  
Mixed Integer ◽  
Fixed Cost ◽  
Hub Location ◽  
Goal Programming Model

Medicine unreliability problem is taken into consideration as one of the most important issues in health supply chain management. This research is associated with the development of a multiobjective optimization problem for the selection of suppliers and distributors. To achieve the purposes, the optimal quota allocation is determined with respect to disruption of suppliers in a five-echelon supply chain network and consideration of the distributor centers as a hub location-allocation mode. The objective of the optimization model is involved in simultaneous minimization of transactions costs dealing with suppliers, expected purchasing costs from suppliers, expected percentages of delayed and returned products in each distributor, as well as transportation cost in each echelon and fixed cost for distributor centers, and finally maximization of the expected scores for suppliers and high priority of product customers. The optimization problem is formulated as a mixed-integer nonlinear programming model. The proposed optimization model is utilized to investigate a numerical case study for asthma-specific medicines. The analyzing procedure is conducted based on the collected real data from Cobel Darou pharmaceutical company in 2019. Furthermore, a fuzzy multichoice goal programming model is considered to solve the proposed optimization model by R optimization solver. The numerical results confirmed the authenticity of the model.

scholarly journals An Optimization Model for Tramp Ship Scheduling considering Time Window and Seaport Operation Delay Factors

2021 ◽  
Vol 2021 ◽  
pp. 1-19
Author(s):  
Ang Yang ◽  
Yu Cao ◽  
Kang Chen ◽  
Qingcheng Zeng ◽  
Zigen Chen
Keyword(s):  
Power Plant ◽  
Thermal Power Plant ◽  
Large Scale ◽  
Time Window ◽  
Programming Model ◽  
Thermal Power ◽  
Scale Model ◽  
Mixed Integer ◽  
Ship Scheduling ◽  
Operation Cost

The quantity of electrical coal transported through the tramp shipping network is increasing due to the high demands. This trend has increased the scheduling difficulty combined with the underdevelopment of the private thermal power plant port. The high coal consumption and low port storage capacity requires the scheduling of the tramp ship to be on a strict time window to ensure the continuous operation of the thermal power plant. The low port unloading capacity often leads to the port congestion and delay of the unloading operation. This paper develops a mixed-integer-programming model for the optimization of the tramp ship scheduling to reduce the total operation cost, including the transportation cost and the unloading waiting cost, and the branch-and-price algorithm is adopted to solve this large-scale model. The model and algorithm are tested with historical operation data from the thermal power plant in the southern coastal areas of China. The optimized scheme significantly reduces the total operation cost by reducing the unloading waiting time and the number of active vessels in certain periods. The results also demonstrate the algorithm improvement in the aspects of the optimization quality and efficiency comparing with the heuristic solution.

scholarly journals Simultaneous Optimization of Road Tolls and Tradable Credits in Public-private Mixed Networks

2017 ◽  
Vol 29 (6) ◽  
pp. 603-611 ◽  
Author(s):  
Nan Jiang ◽  
Xiaoning Zhang ◽  
Hua Wang
Keyword(s):  
Traffic Congestion ◽  
Programming Model ◽  
User Equilibrium ◽  
Simultaneous Optimization ◽  
Management Policy ◽  
Mixed Integer ◽  
Private Company ◽  
Mixed Integer Linear Program ◽  
The Government ◽  
Tradable Credits

This paper investigates a hybrid management policy of road tolls and tradable credits in mixed road networks with both public and private roads. In the public sub-network, a tradable credit scheme is applied to mitigate traffic congestion. In the private sub-network, tolls are collected by the private company, but the toll levels and toll locations are determined by the government. The purpose of toll charge is two-fold: on the one hand, the government uses it as a tool for mitigating congestion; on the other hand, a threshold of revenue should be guaranteed for the profitability of the private company. A bi-level programming model is formulated to minimize the total travel time in the network by taking into account the user equilibrium travel behaviour and the revenue requirement of private firms. To obtain a  global optimum solution, the bi-level model is transformed into an equivalent single-level mixed integer linear program that can be easily solved with commercial software. Numerical examples are provided to demonstrate the effectiveness of the developed model and the efficiency of the proposed algorithm. It is shown that the mixed management schemes can achieve favourable targets, namely, joint implementation of road tolls and tradable credits can effectively mitigate traffic congestion and meanwhile maintain reasonable revenue for the private company.

scholarly journals A Simulated Annealing Methodology to Multiproduct Capacitated Facility Location with Stochastic Demand

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Jin Qin ◽  
Hui Xiang ◽  
Yong Ye ◽  
Linglin Ni
Keyword(s):  
Simulated Annealing ◽  
Facility Location ◽  
Programming Model ◽  
Transportation Cost ◽  
Mixed Integer ◽  
Optimal Decision ◽  
Location Decision ◽  
Inventory Cost ◽  
Stochastic Demands ◽  
Capacitated Facility Location

A stochastic multiproduct capacitated facility location problem involving a single supplier and multiple customers is investigated. Due to the stochastic demands, a reasonable amount of safety stock must be kept in the facilities to achieve suitable service levels, which results in increased inventory cost. Based on the assumption of normal distributed for all the stochastic demands, a nonlinear mixed-integer programming model is proposed, whose objective is to minimize the total cost, including transportation cost, inventory cost, operation cost, and setup cost. A combined simulated annealing (CSA) algorithm is presented to solve the model, in which the outer layer subalgorithm optimizes the facility location decision and the inner layer subalgorithm optimizes the demand allocation based on the determined facility location decision. The results obtained with this approach shown that the CSA is a robust and practical approach for solving a multiple product problem, which generates the suboptimal facility location decision and inventory policies. Meanwhile, we also found that the transportation cost and the demand deviation have the strongest influence on the optimal decision compared to the others.

scholarly journals Wave Planning for Cart Picking in a Randomized Storage Warehouse

2020 ◽  
Vol 10 (22) ◽  
pp. 8050
Author(s):  
Jiun-Yan Shiau ◽  
Jie-An Huang
Keyword(s):  
Best Practice ◽  
Time Window ◽  
Programming Model ◽  
Value Added ◽  
Order Picking ◽  
Mixed Integer ◽  
Just In Time ◽  
Distribution Centers ◽  
Truck Loading ◽  
Planning Algorithm

Randomized storage strategy is known as a best practice for storing books of an online bookstore, it simplifies the order picking strategy as to retrieve books in purchase orders from closest locations of the warehouse. However, to be more responsive to customers, many distribution centers have adopted a just-in-time strategy leading to various value-added activities such as kitting, labelling, product or order assembly, customized packaging, or palletization, all of which must be scheduled and integrated in the order-picking process, and this is known as wave planning. In this study, we present a wave planning mathematical model by simultaneously consider: (1) time window from master of schedule (MOS), (2) random storage stock-keeping units (SKUs), and (3) picker-to-order. A conceptual simulation, along with a simplified example for the proposed wave planning algorithm, has been examined to demonstrate the merits of the idea. The result shows the wave planning module can improve the waiting time for truck loading of packages significantly and can reduce the time that packages are heaping in buffer area. The main contribution of this research is to develop a mixed integer programming model that helps the bookseller to generate optimal wave picking lists for a given time window.

scholarly journals Vehicle Routing Problem for Collaborative Multidepot Petrol Replenishment under Emergency Conditions

2021 ◽  
Vol 2021 ◽  
pp. 1-20
Author(s):  
Guangcan Xu ◽  
Qiguang Lyu
Keyword(s):  
Vehicle Routing ◽  
Time Window ◽  
Programming Model ◽  
Distribution Network ◽  
Mixed Integer ◽  
Small Scale ◽  
Routing Problem ◽  
Routing Optimization ◽  
Multiobjective Particle Swarm Optimization ◽  
Vehicle Routing Optimization

In recent years, emergency events have affected urban distribution with increasing frequency. For example, the 2019 novel coronavirus has caused a considerable impact on the supply guarantee of important urban production and living materials, such as petrol and daily necessities. On this basis, this study establishes a dual-objective mixed-integer linear programming model to formulate and solve the cooperative multidepot petrol emergency distribution vehicle routing optimization problem with multicompartment vehicle sharing and time window coordination. As a method to solve the model, genetic variation of multiobjective particle swarm optimization algorithm is considered. The effectiveness of the proposed method is analyzed and verified by first using a small-scale example and then investigating a regional multidepot petrol distribution network in Chongqing, China. Cooperation between petrol depots in the distribution network, customer clustering, multicompartment vehicle sharing, time window coordination, and vehicle routing optimization under partial road blocking conditions can significantly reduce the total operation cost and shorten the total delivery time. Meanwhile, usage of distribution trucks is optimized in the distribution network, that is, usage of single- and double-compartment trucks is reduced while that of three-compartment trucks is increased. This approach provides theoretical support for relevant government departments to improve the guarantee capability of important materials in emergencies and for relevant enterprises to improve the efficiency of emergency distribution.

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