Formulations and branch-and-cut algorithms for multi-product multi-vehicle production routing problems with startup cost

Qiu, Yuzhuo; Wang, Liang; Xu, Xiaoling; Fang, Xuanjing; Pardalos, Pãnos M.

doi:10.1016/j.eswa.2018.01.006

Cited by 38 publications

(17 citation statements)

References 28 publications

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“…have recently introduced three B&C algorithms to deal with various PRP. The first is a PRP in which reverse logistics and remanufacturing are considered [22] , the second is a problem of production and distribution of perishable products [23] and the third is a problem of production of several types of products with the use of several homogeneous vehicles and taking into account the setting cost when there is production or when moving from the production of one product to another [24]. For the exact method using Bender's decomposition see [25].…”

Section: Literature Reviewsmentioning

confidence: 99%

“…5) + 1 Ɐ (i, j) ∈A( ), Ɐ k ∈ K, Ɐ , t∈ T (24) . with these constraints, each arc is crossed only once and in one direction by a vehicle.…”

Section: )mentioning

confidence: 99%

“…To solve the problem, constraints (20), (21), (22), (23), (24), (24), (25), (26) are added to the initial model defined by the objective function (1) and constraints (2) - (19) and let the SECs (17) drop .The relaxation of the linear program (LP) is then resolved. the approach to eliminate subtours is divided into two stages.…”

Section: B B and C Algorithmmentioning

confidence: 99%

“…Tests on six branching orders allowed us to choose the following order: connection is made on the variables first then to variables and finally to the variables as in [24], [34].The B&C algorithm developed here can therefore be summarized in Fig. 4…”

Section: F Priority Order On Binary Variablementioning

confidence: 99%

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Managing an External Depot in a Production Routing Problem

Kayé¹,

Diaby²,

N'Takpé³

et al. 2020

IJACSA

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This paper addresses a production and distribution problem in a supply chain. The supply chain consists of a plant with no storage capacity that produces only one type of product. The manufactured products are then transported to a depot for storage. Customers demand is met by a homogeneous fleet of vehicles that begins and ends their trips at the depot. The objective of the study is to minimize the overall cost of production, inventory and transport throughout the supply chain. A Branch-and-Cut and a hybrid Two Phases Decomposition Heuristic using a Mixed Integer Programming and a Genetic Algorithm have been developed to solve the problem.

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Section: Literature Reviewsmentioning

confidence: 99%

“…5) + 1 Ɐ (i, j) ∈A( ), Ɐ k ∈ K, Ɐ , t∈ T (24) . with these constraints, each arc is crossed only once and in one direction by a vehicle.…”

Section: )mentioning

confidence: 99%

Section: B B and C Algorithmmentioning

confidence: 99%

Section: F Priority Order On Binary Variablementioning

confidence: 99%

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Managing an External Depot in a Production Routing Problem

Kayé¹,

Diaby²,

N'Takpé³

et al. 2020

IJACSA

View full text Add to dashboard Cite

show abstract

“…The optimization model proposed by Nananukul [5] is also considered the benchmark model for solving the PRP, in which case a single commodity is made available at a single plant and then transported to multiple retailers by multiple homogeneous vehicles in multiple periods. Compared with the PRP solved by the generalized model, the rich PRPs refer to the more complicated situations, such as multiple products, multiple manufacturers, and multiple heterogeneous vehicles [26] [27] [28] [29]. It is important that the vendor managed inventory policy (VMI) is usually adopted in the rich PRPs, in which the retailer's inventory is managed uniformly by the center supplier or manufacturer, and, on the basis of the inventory level, the follow-up production plans and replenishment plans are made accordingly [14].…”

Section: Introductionmentioning

confidence: 99%

The Production Routing Problem Under Uncertain Environment

Wang

Yang

2021

IEEE Access

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To reduce waste and cost as well as to promote the efficient and sustainable use of resources in supply chains, a common focus of practice and research is to integrate decisions of various operational processes such as production, inventory, distribution, and routing. The question of which is more representative of the production routing problem (PRP) then arises. In this paper, the focus is on uncertainty in the PRP, and demand and cost uncertainty are considered simultaneously. According to different decision criteria, three uncertain programming models are correspondingly formulated. Then, through researching the crisp equivalence and conversion of the proposed uncertain models, a series of crisp equivalent models are proposed under the assumptions of particular uncertainty distributions, such as linear, zigzag, and normal distributions. To verify the accuracy and usefulness of the proposed models put forward in this paper, a series of experiments are conducted. Finally, several interesting managerial aspects with respect to the relationship between the confidence level and variance of uncertain variables that are gained from the numerical experiments are highlighted. First, the overall cost of PRP grows as the confidence level increases under uncertain environment. Second, the probability that the optimal total cost of the PRP is less than or equal to a given threshold strictly increases as the threshold increases under uncertain environment. Third, in circumstances such as higher confidence level that decision makers generally pay more attention to, the growth of the variance of uncertain variables may lead to the increase of the total cost. INDEX TERMS Integrated supply chain, production routing problem, uncertain variable, uncertain programming.

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Vehicle Routing Problem with Multi Depot, Heterogeneous Fleet, and Multi Period: A Real Case Study

Karadeniz

Erdogan

Kazançoğlu

2021

Lecture Notes in Mechanical Engineering

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In this study, we designed a vehicle routing problem model for chocolate manufacturer company to optimize their distribution system. Our model includes 3 warehouses, heterogenous fleet, multi period, and about two dozen customers. We got our customer data directly from company. We considered distance between the customers, their demands and vehicle capacities. Sensitivity analysis was made to make sure that it is effective. In the model that we designed, we compared 10-15-20 customer cases with 3 depot, 1 depot, heterogeneous and homogeneous fleet scenarios. According to results of the study homogeneous fleet scenarios are costly. In addition, using single depot is costly than using multiple depots.

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Formulations and branch-and-cut algorithms for multi-product multi-vehicle production routing problems with startup cost

Cited by 38 publications

References 28 publications

Managing an External Depot in a Production Routing Problem

Managing an External Depot in a Production Routing Problem

The Production Routing Problem Under Uncertain Environment

Vehicle Routing Problem with Multi Depot, Heterogeneous Fleet, and Multi Period: A Real Case Study

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