scholarly journals Integrated Harvest and Farm-to-Door Distribution Scheduling with Postharvest Quality Deterioration for Vegetable Online Retailing

Agronomy ◽  
2019 ◽  
Vol 9 (11) ◽  
pp. 724
Author(s):  
Yiping Jiang ◽  
Bei Bian ◽  
Lingling Li
Keyword(s):  
Genetic Algorithm ◽  
Numerical Experiments ◽  
Programming Model ◽  
Time Windows ◽  
Sensitivity Analyses ◽  
Postharvest Quality ◽  
Online Retailing ◽  
Quality Deterioration ◽  
Distribution Scheduling ◽  
Proposed Model

With the rise of vegetable online retailing in recent years, the fulfillment of vegetable online orders has been receiving more and more attention. This paper addresses an integrated optimization model for harvest and farm-to-door distribution scheduling for vegetable online retailing. Firstly, we capture the perishable property of vegetables, and model it as a quadratic postharvest quality deterioration function. Then, we incorporate the postharvest quality deterioration function into the integrated harvest and farm-to-door distribution scheduling and formulate it as a quadratic vehicle routing programming model with time windows. Next, we propose a genetic algorithm with adaptive operators (GAAO) to solve the model. Finally, we carry out numerical experiments to verify the performance of the proposed model and algorithm, and report the results of numerical experiments and sensitivity analyses.

scholarly journals Integrated Tomato Picking and Distribution Scheduling Based on Maturity

2020 ◽  
Vol 12 (19) ◽  
pp. 7934
Author(s):  
Anqi Zhu ◽  
Bei Bian ◽  
Yiping Jiang ◽  
Jiaxiang Hu
Keyword(s):  
Genetic Algorithm ◽  
Time Windows ◽  
Joint Optimization ◽  
Adaptive Genetic Algorithm ◽  
Maturity Model ◽  
Improved Genetic Algorithm ◽  
Routing Problem ◽  
Order Processing ◽  
Supply Chain Efficiency ◽  
Distribution Scheduling

Agriproducts have the characteristics of short lifespan and quality decay due to the maturity factor. With the development of e-commerce, high timelines and quality have become a new pursuit for agriproduct online retailing. To satisfy the new demands of customers, reducing the time from receiving orders to distribution and improving agriproduct quality are significantly needed advancements. In this study, we focus on the joint optimization of the fulfillment of online tomato orders that integrates picking and distribution simultaneously within the context of the farm-to-door model. A tomato maturity model with a firmness indicator is proposed firstly. Then, we incorporate the tomato maturity model function into the integrated picking and distribution schedule and formulate a multiple-vehicle routing problem with time windows. Next, to solve the model, an improved genetic algorithm (the sweep-adaptive genetic algorithm, S-AGA) is addressed. Finally, we prove the validity of the proposed model and the superiority of S-AGA with different numerical experiments. The results show that significant improvements are obtained in the overall tomato supply chain efficiency and quality. For instance, tomato quality and customer satisfaction increased by 5% when considering the joint optimization, and the order processing speed increased over 90% compared with traditional GA. This study could provide scientific tomato picking and distribution scheduling to satisfy the multiple requirements of consumers and improve agricultural and logistics sustainability.

scholarly journals Soft Computing Methodology to Optimize the Integrated Dynamic Models of Cellular Manufacturing Systems in a Robust Environment

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Amir-Mohammad Golmohammadi ◽  
Hasan Rasay ◽  
Zaynab Akhoundpour Amiri ◽  
Maryam Solgi ◽  
Negar Balajeh
Keyword(s):  
Machine Learning ◽  
Programming Model ◽  
Facility Layout ◽  
Cell Formation ◽  
Layout Design ◽  
Metaheuristic Algorithms ◽  
Sensitivity Analyses ◽  
Mixed Integer ◽  
Facility Layout Design ◽  
Proposed Model

Machine learning, neural networks, and metaheuristic algorithms are relatively new subjects, closely related to each other: learning is somehow an intrinsic part of all of them. On the other hand, cell formation (CF) and facility layout design are the two fundamental steps in the CMS implementation. To get a successful CMS design, addressing the interrelated decisions simultaneously is important. In this article, a new nonlinear mixed-integer programming model is presented which comprehensively considers solving the integrated dynamic cell formation and inter/intracell layouts in continuous space. In the proposed model, cells are configured in flexible shapes during the planning horizon considering cell capacity in each period. This study considers the exact information about facility layout design and material handling cost. The proposed model is an NP-hard mixed-integer nonlinear programming model. To optimize the proposed problem, first, three metaheuristic algorithms, that is, Genetic Algorithm (GA), Keshtel Algorithm (KA), and Red Deer Algorithm (RDA), are employed. Then, to further improve the quality of the solutions, using machine learning approaches and combining the results of the aforementioned algorithms, a new metaheuristic algorithm is proposed. Numerical examples, sensitivity analyses, and comparisons of the performances of the algorithms are conducted.

scholarly journals Integrated Optimization on Assortment Packing and Collaborative Shipping for Fashion Clothing

2018 ◽  
Vol 2018 ◽  
pp. 1-10
Author(s):  
Qianying Wang ◽  
Yiping Jiang ◽  
Yang Liu
Keyword(s):  
Numerical Experiments ◽  
Optimization Problem ◽  
Programming Model ◽  
Social Resources ◽  
Mixed Integer ◽  
Integrated Optimization ◽  
Proposed Model ◽  
Integer Nonlinear Programming ◽  
Nonlinear Programming Model ◽  
Effectiveness And Efficiency

With the diversification of customer’s demand and the shortage of social resources, meeting diverse requirements of customers and reducing logistics costs have attracted great attention in logistics area. In this paper, we address an integrated optimization problem that combines fashion clothing assortment packing with collaborative shipping simultaneously. We formulate this problem as a mixed integer nonlinear programming model (MINLP) and then convert the proposed model into a simplified model. We use LINGO 11.0 to solve the transformed model. Numerical experiments have been conducted to verify the effectiveness and efficiency of the proposed model, and the numerical results show that the proposed model is beneficial to the fashion clothing assortment packing and collaborative shipping planning.

scholarly journals Allocating Freight Empty Cars in Railway Networks with Dynamic Demands

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Ce Zhao ◽  
Lixing Yang ◽  
Shukai Li
Keyword(s):  
Genetic Algorithm ◽  
Optimization Model ◽  
Numerical Experiments ◽  
Transportation Cost ◽  
Allocation Problem ◽  
Total Cost ◽  
Storage Cost ◽  
Proposed Model ◽  
Railway Networks

This paper investigates the freight empty cars allocation problem in railway networks with dynamic demands, in which the storage cost, unit transportation cost, and demand in each stage are taken into consideration. Under the constraints of capacity and demand, a stage-based optimization model for allocating freight empty cars in railway networks is formulated. The objective of this model is to minimize the total cost incurred by transferring and storing empty cars in different stages. Moreover, a genetic algorithm is designed to obtain the optimal empty cars distribution strategies in railway networks. Finally, numerical experiments are given to show the effectiveness of the proposed model and algorithm.

scholarly journals Berth Allocation and Quay Crane Assignment Under Uncertainties

2020 ◽  
Author(s):  
Caimao Tan ◽  
Junliang He ◽  
Yuancai Wang
Keyword(s):  
Stochastic Programming ◽  
Numerical Experiments ◽  
Programming Model ◽  
Berth Allocation ◽  
Berth Allocation Problem ◽  
Operations Planning ◽  
Proposed Model ◽  
Departure Time ◽  
Stochastic Programming Model ◽  
Quay Crane Assignment

The integration of berth allocation problem (BAP) and quay crane assignment problem (QCAP) is an cardinal seaside operations planning, which is susceptible to uncertainties, e.g. uncertain vessels arrival and maritime market. This paper addresses the integrated optimization of BAP and QCAP under uncertainties. A stochastic programming model is formulated for minimizing the waiting time and delay departure time of vessels. Besides, numerical experiments and scenario analysis are conducted to validate the effectiveness of the proposed model.

Resource levelling in projects considering different activity execution modes and splitting

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mohammad Khalilzadeh
Keyword(s):  
Genetic Algorithm ◽  
Large Scale ◽  
Programming Model ◽  
Exact Method ◽  
Computational Time ◽  
Resource Usage ◽  
Optimal Solutions ◽  
Content Type ◽  
Proposed Model ◽  
Multi Mode

Purpose This study aims to develop a mathematical programming model for preemptive multi-mode resource-constrained project scheduling problems in construction with the objective of levelling resources considering renewable and non-renewable resources. Design/methodology/approach The proposed model was solved by the exact method and the genetic algorithm integrated with the solution modification procedure coded with MATLAB software. The Taguchi method was applied for setting the parameters of the genetic algorithm. Different numerical examples were used to show the validation of the proposed model and the capability of the genetic algorithm in solving large-sized problems. In addition, the sensitivity analysis of two parameters, including resource factor and order strength, was conducted to investigate their impact on computational time. Findings The results showed that preemptive activities obtained better results than non-preemptive activities. In addition, the validity of the genetic algorithm was evaluated by comparing its solutions to the ones of the exact methods. Although the exact method could not find the optimal solution for large-scale problems, the genetic algorithm obtained close to optimal solutions within a short computational time. Moreover, the findings demonstrated that the genetic algorithm was capable of achieving optimal solutions for small-sized problems. The proposed model assists construction project practitioners with developing a realistic project schedule to better estimate the project completion time and minimize fluctuations in resource usage during the entire project horizon. Originality/value There has been no study considering the interruption of multi-mode activities with fluctuations in resource usage over an entire project horizon. In this regard, fluctuations in resource consumption are an important issue that needs the attention of project planners.

A flexible mathematical model for crew pairing optimization to generate n-day pairings considering the risk of COVID-19: a real case study

Kybernetes ◽  
2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Bahareh Shafipour-Omrani ◽  
Alireza Rashidi Komijan ◽  
Seyed Jafar Sadjadi ◽  
Kaveh Khalili-Damghani ◽  
Vahidreza Ghezavati
Keyword(s):  
Genetic Algorithm ◽  
Programming Model ◽  
Cost Minimization ◽  
Crew Scheduling ◽  
Mixed Integer ◽  
Medium Size ◽  
Content Type ◽  
Solution Approach ◽  
Proposed Model ◽  
Crew Pairing

PurposeOne of the main advantages of the proposed model is that it is flexible to generate n-day pairings simultaneously. It means that, despite previous researches, one-day to n-day pairings can be generated in a single model. The flexibility in generating parings causes that the proposed model leads to better solutions compared to existing models. Another advantage of the model is minimizing the risk of COVID-19 by limitation of daily flights as well as elapsed time minimization. As airports are among high risk places in COVID-19 pandemic, minimization of infection risk is considered in this model for the first time. Genetic algorithm is used as the solution approach, and its efficiency is compared to GAMS in small and medium-size problems.Design/methodology/approachOne of the most complex issues in airlines is crew scheduling problem which is divided into two subproblems: crew pairing problem (CPP) and crew rostering problem (CRP). Generating crew pairings is a tremendous and exhausting task as millions of pairings may be generated for an airline. Moreover, crew cost has the largest share in total cost of airlines after fuel cost. As a result, crew scheduling with the aim of cost minimization is one of the most important issues in airlines. In this paper, a new bi-objective mixed integer programming model is proposed to generate pairings in such a way that deadhead cost, crew cost and the risk of COVID-19 are minimized.FindingsThe proposed model is applied for domestic flights of Iran Air airline. The results of the study indicate that genetic algorithm solutions have only 0.414 and 0.380 gap on average to optimum values of the first and the second objective functions, respectively. Due to the flexibility of the proposed model, it improves solutions resulted from existing models with fixed-duty pairings. Crew cost is decreased by 12.82, 24.72, 4.05 and 14.86% compared to one-duty to four-duty models. In detail, crew salary is improved by 12.85, 24.64, 4.07 and 14.91% and deadhead cost is decreased by 11.87, 26.98, 3.27, and 13.35% compared to one-duty to four-duty models, respectively.Originality/valueThe authors confirm that it is an original paper, has not been published elsewhere and is not currently under consideration of any other journal.

Emergency Logistics System Based on Bilevel Optimization Model

2013 ◽  
Vol 694-697 ◽  
pp. 3605-3609
Author(s):  
Bo Liu ◽  
Bo Li ◽  
Yan Li
Keyword(s):  
Genetic Algorithm ◽  
Bilevel Programming ◽  
Programming Model ◽  
Hybrid Genetic Algorithm ◽  
Optimal Solution ◽  
Bilevel Optimization ◽  
Logistics System ◽  
Emergency Logistics ◽  
Proposed Model ◽  
Bilevel Programming Model

A bilevel programming model is established to determine the emergency storage centers location and the resource supply plan of the provincial and municipal levels by the collaborative mode of the vertical supply and lateral transfer for the emergency logistics system in the unusual emergencies. And the optimal solution is obtained by the hybrid genetic algorithm. Finally, the case shows the effectiveness of the proposed model and its algorithm.

scholarly journals Routing cross-docking depots, considering the time windows and pricing routes (case study: container transportation of Chabahar port)

2020 ◽  
Vol 33 (02) ◽  
pp. 409-422
Author(s):  
Farhad Bavar ◽  
Majid Sabzehparvar ◽  
Mona Ahmadi Rad
Keyword(s):  
High Efficiency ◽  
Programming Model ◽  
Time Windows ◽  
Container Transportation ◽  
Total Cost ◽  
Cross Docking ◽  
Proposed Model ◽  
Nonlinear Programming Model ◽  
Implementation Time ◽  
The Cost

In this study, we develop a model for routing cross-docking centers considering time windows and pricing routs. In this model picking and delivery in several times is permitted and each knot can be serviced by more than one vehicle. Every truck can transport one or more product, in other words, we consider compatibility between product and vehicle. This model includes two goals: reducing the total cost and reducing the cost of carrying goods (freight fare). The total cost includes the cost required to traverse between the points, the cost of traversing the routes between the central cross-docking center and the first points after moving, and the cost to traverse the routes between the last points in each route and the depots that must be minimized. In general, the purpose of the model is to obtain the number of cross-docking center, the number of vehicles and the best route in the distribution network. We present a nonlinear programming model for this problem. We have solved the proposed model by GAMS. As the dimensions of the problem increase, the implementation time of the program increases progressively. So, in order to solve the model in medium and large scales, we proposed a genetic meta-heuristic algorithm. The results of examining different issues by the meta-heuristic approach show the very high efficiency of the developed algorithms in terms of the solution time and the answer of the problem.

Routing Optimization of City Distribution Considering Access Restriction

2014 ◽  
Vol 505-506 ◽  
pp. 959-966 ◽  
Author(s):  
Xin Jin ◽  
Yang Tang ◽  
Qi Xu
Keyword(s):  
Genetic Algorithm ◽  
Optimization Model ◽  
Numerical Experiments ◽  
Benchmark Problems ◽  
Local Authorities ◽  
Urban Freight ◽  
Routing Optimization ◽  
Proposed Model ◽  
Urban Freight Transport ◽  
Negative Impacts

Nowadays, urban freight transport has become one of the most significant contributors to congestion and pollution. To combat these negative impacts, local authorities implement some restrictive regulations imposed on logistic enterprises about the time and area to which their vehicles can access. Under this circumstance, this paper constructed a city distribution routing optimization model considering access restriction. A Genetic Algorithm was proposed to solve the model. Numerical experiments based on the Solomons benchmark problems proved the effectiveness of the proposed model and algorithms.

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