Mixed integer programming formulations for the Biomass Truck Scheduling problem

Torjai, László; Kruzslicz, Ferenc

doi:10.1007/s10100-015-0395-6

Cited by 17 publications

(6 citation statements)

References 29 publications

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“…Besides, some articles examine the routing or scheduling decision alone. Torjai and Kruzslicz [49] developed a model to schedule trucks to deliver biomass from satellite storage locations to a central biorefinery. Soares et al [50] investigated the synchronization of trucks' movement and operation in a full truck pick and delivery problem.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimizing Palm Oil Biomass Supply Chain Logistics through Multi-Objective Location-Routing Model

Fong Yeng,

Zainuddin,

See Pheng

2024

Mal. J. Fund. Appl. Sci.

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Malaysia can convert agricultural wastes (biomass) into biofuel to reduce fossil fuel dependency and solve the disposal problem. As one of the largest palm oil producers, Malaysia has an abundance of palm oil biomass, but the biomass has high humidity, low energy density, and is scattered geographically. Establishing collection facilities with pretreatment operations is suggested to collect the biomass and improve its quality. Nevertheless, the facility placement and vehicle routing decisions significantly affect the total cost and operational efficiency. Hence, this study develops a model to address the location-routing problem and quantifies the pretreatment operation to customize the process in the biomass supply chain. This research also addresses sustainability from all dimensions through multi-objective optimization. The model minimizes costs, reduces negative social impacts by considering population densities, and measures environmental performance through CO2 emissions. The study first optimized each objective function separately and then conducted a multi-objective optimization using a weighted sum approach. Optimizing each objective function individually will achieve the best outcome for each dimension, but enhancing one objective would impair the others. However, multi-objective optimization shows some compensation for the performances where economic, social, and environmental indicator values decreased by 0.36%, 6.58%, and 15.28%, respectively. The results demonstrate that the model adjusts the locational and routing decisions based on different goals.

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Section: Literature Reviewmentioning

confidence: 99%

Optimizing Palm Oil Biomass Supply Chain Logistics through Multi-Objective Location-Routing Model

Fong Yeng,

Zainuddin,

See Pheng

2024

Mal. J. Fund. Appl. Sci.

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“…In [26], the authors applied the classical vehicle routing problem to the biomass collection problem in order to determine the routes with minimal costs to the vehicle fleets involved in the biomass supply chain. In [27], three mixed integer programming formulations were presented to solve the truck scheduling problem. For more detailed information, [28] presented a survey on the models developed for biomass supply chains from an operations research perspective, and [18] presented the modelling approaches to optimize economic, social, and environmental criteria in these supply chains.…”

Section: Complexitymentioning

confidence: 99%

A Multilayer Model Predictive Control Methodology Applied to a Biomass Supply Chain Operational Level

et al. 2017

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Forest biomass has gained increasing interest in the recent years as a renewable source of energy in the context of climate changes and continuous rising of fossil fuels prices. However, due to its characteristics such as seasonality, low density, and high cost, the biomass supply chain needs further optimization to become more competitive in the current energetic market. In this sense and taking into consideration the fact that the transportation is the process that accounts for the higher parcel in the biomass supply chain costs, this work proposes a multilayer model predictive control based strategy to improve the performance of this process at the operational level. The proposed strategy aims to improve the overall supply chain performance by forecasting the system evolution using behavioural dynamic models. In this way, it is possible to react beforehand and avoid expensive impacts in the tasks execution. The methodology is composed of two interconnected levels that closely monitor the system state update, in the operational level, and delineate a new routing and scheduling plan in case of an expected deviation from the original one. By applying this approach to an experimental case study, the concept of the proposed methodology was proven. This novel strategy enables the online scheduling of the supply chain transport operation using a predictive approach.

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“…The setup time resulting from the setup operation can be defined as the time required to prepare the necessary resources to perform a task [3]. The PMSSS problem has various applications, e.g., in supply chain [9,35], in plastic injection industry [6], in printing industry [24], in semiconductor industry [27], and recently in health care [23].…”

Section: Introductionmentioning

confidence: 99%

General variable neighborhood search for the parallel machine scheduling problem with two common servers

2022

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We address in this paper the parallel machine scheduling problem with a shared loading server and a shared unloading server. Each job has to be loaded by the loading server before being processed on one of the available machines and unloaded immediately by the unloading server after its processing. The objective function involves the minimization of the overall completion time, known as the makespan. This important problem raises in flexible manufacturing systems, automated material handling, healthcare, and many other industrial fields, and has been little studied up to now. To date, research on it has focused on the case of two machines. The regular case of this problem is considered. A mixed integer programming formulation based on completion time variables is suggested to solve small-sized instances of the problem. Due to its $$\mathcal{NP}\mathcal{}$$ NP -hardness, we propose two greedy heuristics based on the minimization of the loading, respectively unloading, server waiting time, and an efficient General Variable Neighborhood Search (GVNS) algorithm. In the computational experiments, the proposed methods are compared using 120 new and publicly available instances. It turns out that, the proposed GVNS with an initial solution-finding mechanism based on the unloading server waiting time minimization significantly outperforms the other approaches.

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Mixed integer programming formulations for the Biomass Truck Scheduling problem

Cited by 17 publications

References 29 publications

Optimizing Palm Oil Biomass Supply Chain Logistics through Multi-Objective Location-Routing Model

Optimizing Palm Oil Biomass Supply Chain Logistics through Multi-Objective Location-Routing Model

A Multilayer Model Predictive Control Methodology Applied to a Biomass Supply Chain Operational Level

General variable neighborhood search for the parallel machine scheduling problem with two common servers

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