Multi-objective inventory routing problem: A stochastic model to consider profit, service level and green criteria

Rahimi, Mohammad; Baboli, Armand; Rekik, Yacine

doi:10.1016/j.tre.2017.03.001

Cited by 77 publications

(46 citation statements)

References 43 publications

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“…Discussions under the scope of autonomous also involve batteries and fuel cell based equipment [47]. In this sense, there is a trend in researching more sustainable transports connected with the achievement of higher profits [48], which are concepts that should match for the achievement of a widespread adoption by the industry.…”

Section: Smart Routing Problems: Multi-objective Optimizationmentioning

confidence: 99%

Mobility, Citizens, Innovation and Technology in Digital and Smart Cities

et al. 2020

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Cities are constantly transforming and, consequently, attracting efforts from researchers and opportunities to the industry. New transportation systems are being built in order to meet sustainability and efficiency criteria, as well as being adapted to the current possibilities. Moreover, citizens are becoming aware about the power and possibilities provided by the current generation of autonomous devices. In this sense, this paper presents and discusses state-of-the-art transportation technologies and systems, highlighting the advances that the concepts of Internet of Things and Value are providing. Decentralized technologies, such as blockchain, are been extensively investigated by the industry, however, its widespread adoption in cities is still desirable. Aligned with operations research opportunities, this paper identifies different points in which cities’ services could move to. This also study comments about different combinatorial optimization problems that might be useful and important for an efficient evolution of our cities. By considering different perspectives, didactic examples are presented with a main focus on motivating decision makers to balance citizens, investors and industry goals and wishes.

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Section: Smart Routing Problems: Multi-objective Optimizationmentioning

confidence: 99%

Mobility, Citizens, Innovation and Technology in Digital and Smart Cities

et al. 2020

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“…In many studies, the material distribution problem mainly considers the vehicle load, ignoring the uncertainty of the number of vehicles (operators) [17,18]. The model also considers the number of vehicles (operators), and the experiment results are closer to reality than the general material distribution problem.…”

Section: Discussionmentioning

confidence: 99%

“…In order to optimize the material distribution paths for reducing cost of production, and improving market competitiveness of enterprises, some researchers have established different multi-objective model of material distribution path based on the different objectives and impact factors [16]. For example, Mohammad Rahimi [17] posed a new idea about inventory path, based on the level of service in the distribution of perishable products-considered by determining the specific expiration date, and established a multi-objective mathematical model to maximize profit, minimize delay rate, and minimize the number of vehicles. Keivan Ghoseiri [18] established a multi-objective path optimization model, which can minimize the total cost of transportation and transit time.…”

Section: Research Concerning Materials Distribution Optimizationmentioning

confidence: 99%

“…The problem of the material distribution path is usually optimized with a single goal such as minimizing transportation cost or minimizing distribution distance [17][18][19]. However, manufacturers hope to achieve multiple objectives of carbon emissions reduction while saving cost, so it is necessary to establish a multi-objective optimization model.…”

Section: Model Formulationmentioning

confidence: 99%

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Carbon Emission Reduction of Apparel Material Distribution Based on Multi-Objective Genetic Algorithm (NSGA-II)

2019

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Low-carbon production has become one of the top management objectives for every industry. In garment manufacturing, the material distribution process always generates high carbon emissions. In order to reduce carbon emissions and the number of operators to meet enterprises’ requirements to control the cost of production and protect the environment, the paths of material distribution were analyzed to find the optimal solution. In this paper, the model of material distribution to obtain minimum carbon emissions and vehicles (operators) was established to optimize the multi-target management in three different production lines (multi-line, U-shape two-line, and U-shape three-line), while the workstations were organized in three ways: in the order of processes, in the type of machines, and in the components of garment. The NSGA-II algorithm (non-dominated sorting genetic algorithm-II) was applied to obtain the results of this model. The feasibility of the model and algorithm was verified by the practice of men’s shirts manufacture. It could be found that material distribution of multi-line layout produced the least carbon emissions when the machines were arranged in the group of type.

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“…Soysal (2016) use CPLEX to solve a small case study and, based on a simulation experiment, confirm the benefit of including uncertainty in the model. Rahimi et al (2017) describe a rich IRP with environmental considerations and stochastic parameters, including stochastic demand, and propose a fuzzy approach. Their solution methodology relies on a meta-heuristic from the literature.…”

Section: Rich Vehicle and Inventory Routing Problemsmentioning

confidence: 99%

A unified framework for rich routing problems with stochastic demands

Markov

Bierlaire

Cordeau

et al. 2018

Transportation Research Part B: Methodological

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We introduce a unified framework for rich vehicle and inventory routing problems with complex physical and temporal constraints. Demands are stochastic, can be non-stationary, and are forecast using any model that provides the expected demands and their error term distribution, which can be any theoretical or empirical distribution. We offer a detailed discussion on the modeling of demand stochasticity, focusing on the probabilities and cost effects of undesirable events, such as stock-outs, breakdowns and route failures, and their associated recourse actions. Tractability is achieved through the ability to pre-compute or at least partially pre-process the stochastic information, which is possible under mild assumptions for a general inventory policy. We integrate the stochastic aspect into a mixed integer non-linear program, illustrate applications to various problem classes, and show how to model specific problems through the lens of inventory routing. The case study is based on two sets of realistic instances, representing a waste collection inventory routing problem and a facility maintenance problem, respectively. We analyze the effects of our assumptions on modeling realism and tractability, and demonstrate that our framework significantly outperforms deterministic policies in its ability to limit the number of undesirable events for the same routing cost.

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Multi-objective inventory routing problem: A stochastic model to consider profit, service level and green criteria

Cited by 77 publications

References 43 publications

Mobility, Citizens, Innovation and Technology in Digital and Smart Cities

Mobility, Citizens, Innovation and Technology in Digital and Smart Cities

Carbon Emission Reduction of Apparel Material Distribution Based on Multi-Objective Genetic Algorithm (NSGA-II)

A unified framework for rich routing problems with stochastic demands

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