CLOVES: A cluster-and-search heuristic to solve the vehicle routing problem with delivery and pick-up

Ganesh, K.; Narendran, T.T.

doi:10.1016/j.ejor.2006.01.037

Cited by 78 publications

(21 citation statements)

References 24 publications

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“…Finally, metaheuristic algorithms are increasingly popular owing to the satisfactory compromise between the computational time and solution quality. Some of metaheuristic algorithms applied for resolving GPDP are nature-inspired, such as the genetic algorithm [23] and ant colony optimization [24] . The others are memory-based search strategies, which include GRASP [25] , Tabu search [14] , and scatter search [2] .…”

Section: Pickup and Deliverymentioning

confidence: 99%

Adaptive memory artificial bee colony algorithm for green vehicle routing with cross-docking

Yin

Chuang

2016

Applied Mathematical Modelling

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Available online xxxKeyword: Artificial bee colony algorithm Adaptive memory programing Cross-docking Green supply chain management Vehicle routing a b s t r a c t Cross-docking distribution is becoming increasingly prevalent in supply chain management (SCM) due to the replacement of storage and order-picking with efficient product consolidation. Several successful cross-docking applications have been put into practice by companies such as Wal-Mart, FedEx, and Home Depot. Cross-docking consists of interrelated operations which require proper synchronization. Only a few early attempts have addressed the vehicle routing problem in this context. Moreover, the social consciousness of sustainable development has brought up the issue of green SCM which is not only environment-friendly but also beneficial to business values. This paper first addresses a previously studied problem and then proposes a new problem version for planning the least cost green vehicle routing in which the deployed vehicles transport the final products from suppliers to customers through a cross-dock subject to a CO 2 intensity constraint. We develop an adaptive memory artificial bee colony (AMABC) algorithm to tackle both problems. Compared to a Tabu search proposed in the literature, the AMABC algorithm can reach higher fuel efficiency by managing the loading along the route and yield less cost and CO 2 intensities. Statistical tests of simulation and geographic data show that the AM-ABC method is robust against the problem size and convergence of the objective value is guaranteed with high confidence.

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Section: Pickup and Deliverymentioning

confidence: 99%

Adaptive memory artificial bee colony algorithm for green vehicle routing with cross-docking

Yin

Chuang

2016

Applied Mathematical Modelling

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“…Treatment of vehicle routing problems in conjunction with truck loading has been studied in the literature (see [3] to [6]). The ALMA logistic application concentrates also on dynamic logistic problems whereby dynamicity results from new orders, cancellations, as well as traffic incidents that may occur at any time; this leads to extremely difficult problems.…”

Section: Logisticsmentioning

confidence: 99%

ALMA, A Logistic Mobile Application Based on Internet of Things

Baz

Bourgeois

Saadi

et al. 2013

2013 IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical A

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“…In each cluster obtained, we orient the nodes along a path using the Shrink-Wrap Algorithm (Lawler et al 1985, Ganesh andNarendran, 2007). The nodes are mapped on polar coordinates, sorted by angle (θ), then by distance (ρ) and arranged in ascending order.…”

Section: Second Phase Of Heuristic-shrink Wrap Algorithmmentioning

confidence: 99%

“…In the first stage, we cluster the nodes based on a clustering approach. In the second phase, we used shrink wrap algorithm proposed by Ganesh and Narendran (2007) to form the initial tour. In the third stage, we develop a simulated annealing (SA) approach to improve and balance the tour.…”

Section: Decision Support System Based On Heuristic-a Case Studymentioning

confidence: 99%

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Intelligence heuristics to solve a Balanced Routing Problem in Supply Chain

Kannan¹,

Jayanthi²,

Dhanalakshmi³

2017

IJCA

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In today's business system, supply chain competitiveness greatly depends on its capability to handle the challenges of cost reduction, service level improvement and quality enhancement. In this competitive market, customer service level is the most significant factor for the success of the firm. Supply chain network needs to be efficient enough to handle the changing demand patterns. Balanced routing of goods among supply chain entities will improve the asset utilization and customer service level in a supply chain system. Over or under utilization of the supply chain entity will impact the customer service. We develop a decision support system based on three-stage heuristics to solve this balanced routing problem. A case study is illustrated and the DSS is validated for this case study.

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CLOVES: A cluster-and-search heuristic to solve the vehicle routing problem with delivery and pick-up

Cited by 78 publications

References 24 publications

Adaptive memory artificial bee colony algorithm for green vehicle routing with cross-docking

Adaptive memory artificial bee colony algorithm for green vehicle routing with cross-docking

ALMA, A Logistic Mobile Application Based on Internet of Things

Intelligence heuristics to solve a Balanced Routing Problem in Supply Chain

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