Physical distribution activities and vehicle routing problems in logistics management: A case study

Güneri, Ali Fuat

doi:10.1243/09544054jem560

Cited by 8 publications

(2 citation statements)

References 39 publications

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“…In particular CVRP is the so-called capacitated VRP, where a fixed fleet of delivery vehicles of uniform capacity must serve known customer demands from a common depot, e.g. a distribution center CD, at minimum transit cost (Güneri 2007). In SC planning, given a point in time t, e.g.…”

Section: Operational Planningmentioning

confidence: 99%

A Supporting Decision Tool for the Integrated Planning of a Logistic Network

Manzini¹,

Bortolini²,

Gamberi³

et al. 2011

Supply Chain Management - New Perspectives

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Design, management and control of a logistic distribution system are very critical issues in supply chain management (SCM). They involve a large number of interdependent decisions, such as the determination of the best location and capacity of a distribution center (DC), a production plant, a wholesaler etc., the allocation of customer demand to suppliers, e.g. regional DC (RDC), the adoption of a transportation mode, e.g. rail and truck, the vehicle routing adopting/not adopting a "groupage" strategy. This chapter presents an original and automatic supporting decisions platform for the integration of strategic (long-term), tactical (mid-term) and operational (short-term) decisions in the design, management and control of a logistic network including up to four operating levels: sources (production plants), central distribution centers (CDCs), RDCs, and customers (points of demand-Pods). A case study is illustrated and obtained results discussed in presence of different problem settings and operating hypotheses. The main contribution of this chapter is the illustration of an automatic supporting decisions tool for the design, management, control and optimization of a logistic network. The remainder of this chapter is organized as follows. Section 2 presents a review of the literature studies on the design and management of a supply chain. Section 3 illustrates the proposed conceptual framework for planning a supply chain: this framework has been adopted by LD-LogOptimizer. Section 4 presents a case study and illustrates the obtained results. Finally Section 5 presents conclusions and further research. 2. Supply chain planning A supply chain (SC) is a network of suppliers (sources), production plants, warehouses, and distribution channel organized to acquire raw materials, convert them to finished products, and distribute products to customers. The flow of goods between a supplier and customer passes through several levels and stages, and each level consists of many facilities (Bidhandi and Yusuff, 2011). A generic SC network is depicted in Figure 1. It is made of 4 levels (sources, CDCs, RDCs, Customers) and 3 stages (Sources-CDCs, CDCs-RDCs, RDCs-Customers). The generic stage involves two different kinds of entities, e.g. CDCs and RDCs, linked with a direct flow of www.intechopen.com

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Section: Operational Planningmentioning

confidence: 99%

A Supporting Decision Tool for the Integrated Planning of a Logistic Network

Manzini¹,

Bortolini²,

Gamberi³

et al. 2011

Supply Chain Management - New Perspectives

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show abstract

“…The truth that VRP is both of analytical and realistic interest clarifies the amount of focus given to the VRP by researchers over the past years. 1 The VRP can be described as the procedure of computing inexpensive routes for distribution vehicles to carry the necessary products to every single customer with the constraints that each vehicle has to begin and end at the central depot, each customer should be visited by one vehicle precisely and the sum of all customer demand in a route should not surpass vehicle capacity. 2 The vehicle routing problem with time windows (VRPTW) is a variant of VRP.…”

Section: Introductionmentioning

confidence: 99%

Multi-objective vehicle routing problem with time windows: Improving customer satisfaction by considering gap time

Sivaramkumar¹,

Thansekhar²,

Saravanan³

et al. 2015

Proceedings of the Institution of Mechanical Engineers, Part B:

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The vehicle routing problem with time windows is a combinatorial optimisation problem in distribution logistics. It has been infrequently measured as a multi-objective optimisation problem for the benefit of customers. For the purposes of this research, the measurement of multi-objective vehicle routing problem with time windows will be in terms of a minimisation of the total distance travelled by all vehicles, the total number of vehicles used (management beneficial objectives) and the total gap between ready time and issuing time (customer beneficial objective). It is possible to satisfy customers by issuing the goods as close as possible to the customer ready time. This is because possibilities for going out of stock during the above said time gap (by chance of improper inventory maintenance) are reduced with a minimised total time gap, which in turn increases the sales orders for the manufacturing management. An improved genetic algorithm, called the fitness aggregated genetic algorithm, has been implemented to resolve the problem. The proposed algorithm incorporates a fitness aggregation approach and dedicated operators, such as selection based on aggregate fitness value and best cost route crossover, to resolve the multi-objective problem. The algorithm was validated on Solomon’s bi-objective benchmark models for the minimisation of the total distance travelled and total number of vehicles used, and the results formed by proposed algorithm are competitive to best known results. After validating the proposed algorithm on bi-objective models, the third objective – namely, the total time gap between ready time and issuing time – is included in the bi-objective model. The results show that the suggested algorithm creates improved customer-satisfied routes without drastically affecting the total distance travelled and total number of vehicles used.

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Peer-induced fairness capacitated vehicle routing scheduling using a hybrid optimization ACO–VNS algorithm

Pan

et al. 2019

Soft Comput

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Physical distribution activities and vehicle routing problems in logistics management: A case study

Cited by 8 publications

References 39 publications

A Supporting Decision Tool for the Integrated Planning of a Logistic Network

A Supporting Decision Tool for the Integrated Planning of a Logistic Network

Multi-objective vehicle routing problem with time windows: Improving customer satisfaction by considering gap time

Peer-induced fairness capacitated vehicle routing scheduling using a hybrid optimization ACO–VNS algorithm

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