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Transportation Planning in the Product Distribution Chain According to Traffic Criteria

Babaei, Ardavan | 2023

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  1. Type of Document: Ph.D. Dissertation
  2. Language: Farsi
  3. Document No: 56498 (01)
  4. University: Sharif University of Technology
  5. Department: Industrial Engineering
  6. Advisor(s): Akbari Jokar, Mohammad Reza; Khedmati, Majid
  7. Abstract:
  8. Increased costs due to fuel consumption by vehicles, air pollution and greenhouse gas emissions, safety concerns in accidents, social concerns regarding the satisfaction of the users of roads and routes, and increased delivery time of goods are among the important issues that highlight the need to consider traffic congestion in the supply chain planning. In addition, in supply chains goods are generally transported by medium and heavy transportation vehicles. Their higher weights, bigger sizes and lower speeds are among the characteristics of these vehicles that cause traffic congestion. Therefore, it is necessary to address the topic of traffic congestion in the supply chain planning. Despite its importance, only few researchers have investigated supply chain planning from the lens of traffic congestion. This dissertation is an effort to study the topic of traffic congestion in supply chain planning from different angles, with the aim of contributing to the research literature in the fields of traffic and supply chain planning. Thus, this dissertation adopts a comprehensive and category-based approach to address the issue of traffic congestion in supply chain planning optimization models. For this purpose, traffic congestion is examined in both centralized and decentralized supply chain planning modes. In addition, traffic congestion is examined through the lenses of customer areas, service delivery areas and the links between the levels of the supply chain. On the other hand, in supply chain planning, the delivery methods are considered based on the last mile delivery logistics and the number of distribution channels which can influence traffic congestion. For each of these categories, a multi-objective mathematical optimization model is provided. The objective functions include the minimization of supply chain costs, maximization of efficiency, the customer value, and transparency. In order for the mathematical models to produce both efficient and optimized solutions, we integrate the mathematical models with data-oriented models that are based on data-envelopment analysis from the two-phase, integrated and iterative perspectives. The multi-objective models are transformed into single-objective models by using goal programming methods. The presented models take into account uncertain conditions for demand, customer behavior, availability of labor force, transportation costs, and the traffic congestion thresholds. All the presented models are solved by GAMS Win 32 25.1.2. Cost and the level of service are the most important factors that affect the decisions made by supply chain managers, and the proposed mathematical models show pertinent results. The decentralized supply chain planning mathematical model helps to reduce purchasing costs and shortages. The centralized supply chain planning mathematical model which is integrated with the data-oriented model helps to maximize the service level. And the centralized supply chain planning mathematical model with iterative links to the data-oriented model helps to minimize costs. The results show that serving efficient customers and increasing distribution channels between intermittent levels of the supply chain have an important role in developing economic plans for the supply chain. Supply chain managers in various fields (such as food, energy, automobile, municipalities, and environmental protection organizations etc.) can use the models presented in this study to manage and ease traffic congestion in the distribution chains of goods, as well as for supply chain planning
  9. Keywords:
  10. Transportation Planning ; Data Envelopment ; Traffic Congestion ; Efficiency ; Product Distribustion ; Supply Chain Planning

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