• Title/Summary/Keyword: Period Vehicle Routing

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A Clustering Based Approach for Periodic Vehicle Routing Problems (클러스터링을 이용한 주기적 차량운행경로 문제 해법)

  • Kim, Byeong-In;Kim, Seong-Bae;Sahoo, Surya
    • Proceedings of the Korean Operations and Management Science Society Conference
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    • 2005.05a
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    • pp.52-58
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    • 2005
  • In this paper, we address a real-world periodic vehicle routing problem with time windows (PVRPTW). In addition to the general requirements of single-day vehicle routing problem, each stop has required number of visits within a cycle period in PVRPTW. Thus, we need to determine optimized days of visit for each stop with consideration of the cycle-period days together. The problem also requires consistent vehicle assignment to the stops. We developed a clustering based 3-phase approach for this problem: 1) stop-route assignment, 2) stop-day assignment, and 3) stop sequencing within a single-day route. Using the approach, we could reduce the number of routes and improve the routing efficiency for several real-world problems.

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A Heuristic Algorithm for the Periodic Heterogeneous Fleet Vehicle Routing Problem (주기적 다용량 차량경로문제에 관한 발견적 해법)

  • Yoon, Tae-Yong;Lee, Sang-Heon
    • Journal of the Korean Operations Research and Management Science Society
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    • v.36 no.1
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    • pp.27-38
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    • 2011
  • In this paper, we deal with the periodic heterogeneous fleet vehicle routing problem (PHVRP). PHVRP is a problem of designing vehicle routes in each day of given period to minimize the sum of fixed cost and variable cost over the planning horizon. Each customer can be visited once or more times over the planning horizon according to the service combinations of that customer. Due to the complexity of the problem, we suggest a heuristic algorithm in which an initial solution is obtained by assigning the customer-day and the customer-car simultaneously and then it is improved. A performance of the proposed algorithm was compared to both well-known results and new test problems.

Integrated Production-Distribution Planning for Single-Period Inventory Products Using a Hybrid Genetic Algorithm (혼성 유전알고리듬을 이용한 단일기간 재고품목의 통합 생산-분배계획 해법)

  • Park, Yang-Byung
    • IE interfaces
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    • v.16 no.3
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    • pp.280-290
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    • 2003
  • Many firms are trying to optimize their production and distribution functions separately, but possible savings by this approach may be limited. Nowadays, it is more important to analyze these two functions simultaneously by trading off the costs associated with the whole. In this paper, I treat a production and distribution planning problem for single-period inventory products comprised of a single production facility and multiple customers, with the aim of optimally coordinating important and interrelated decisions of production sequencing and vehicle routing. Then, I propose a hybrid genetic algorithm incorporating several local optimization techniques, HGAP, for integrated production-distribution planning. Computational results on test problems show that HGAP is effective and generates substantial cost savings over Hurter and Buer's decoupled planning approach in which vehicle routing is first developed and a production sequence is consequently derived. Especially, HGAP performs better on the problems where customers are dispersed with multi-item demand than on the problems where customers are divided into several zones based on single-item demand.

A Hybrid Genetic Algorithm for Vehicle Routing Problem which Considers Traffic Situations and Stochastic Demands (교통상황과 확률적 수요를 고려한 차량경로문제의 Hybrid 유전자 알고리즘)

  • Kim, Gi-Tae;Jeon, Geon-Uk
    • Journal of Korean Society of Transportation
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    • v.28 no.5
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    • pp.107-116
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    • 2010
  • The vehicle travel time between locations in a downtown is greatly influenced by both complex road conditions and traffic situation that changes real time according to various external variables. The customer's demands also stochastically change by time period. Most vehicle routing problems suggest a vehicle route considering travel distance, average vehicle speed, and deterministic demand; however, they do not consider the dynamic external environment, including items such as traffic conditions and stochastic demand. A realistic vehicle routing problem which considers traffic (smooth, delaying, and stagnating) and stochastic demands is suggested in this study. A mathematical programming model and hybrid genetic algorithm are suggested to minimize the total travel time. By comparing the results considering traffic and stochastic demands, the suggested algorithm gives a better solution than existing algorithms.

A Two-Stage Heuristic for Period Vehicle Routing : Minimizing the Fleet Size (소요차량을 최소화하는 기간차량경로 문제에 관한 2단계 발견적 기법)

  • Cho, Byeong-Min;Kim, Jun-Gyu;Lee, Dong-Ho
    • Journal of Korean Society of Industrial and Systems Engineering
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    • v.31 no.3
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    • pp.90-100
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    • 2008
  • 기간차량경로 문제는 차량용량제약을 고려한 차량경로문제를 다 기간으로 확장한 형태의 문제로 역방향 로지스틱스의 폐기물 혹은 재활용품 수거에 관련된 주요한 운영 문제들 중의 하나로 각 고객에 대해서는 계획기간 중에 방문해야 하는 횟수가 정해져 있어 방문날짜 조합을 결정해야 하며 주어진 방문날짜 조합 하에 각 기간의 차량경로도 결정해야 한다. 주요한 제약조건으로는 차량의 용량제약과 각 기간의 가용 시간제약이 있으며 소요차량의 대수를 최소화하는 것을 목적으로 한다. 본 연구에서는 대상 문제의 복잡도로 인하여 초기해를 구하고 그 해를 개선하는 2 단계 발견적 알고리듬을 제안하였으며 다양한 문제들에 대한 계산실험 결과 본 연구에서 제안하고 있는 발견적 알고리듬이 기존 알고리듬보다 우수함을 보였다.

A Simulation-Based Investigation of an Advanced Traveler Information System with V2V in Urban Network (시뮬레이션기법을 통한 차량 간 통신을 이용한 첨단교통정보시스템의 효과 분석 (도시 도로망을 중심으로))

  • Kim, Hoe-Kyoung
    • Journal of Korean Society of Transportation
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    • v.29 no.5
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    • pp.121-138
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    • 2011
  • More affordable and available cutting-edge technologies (e.g., wireless vehicle communication) are regarded as a possible alternative to the fixed infrastructure-based traffic information system requiring the expensive infrastructure investments and mostly implemented in the uninterrupted freeway network with limited spatial system expansion. This paper develops an advanced decentralized traveler information System (ATIS) using vehicle-to-vehicle (V2V) communication system whose performance (drivers' travel time savings) are enhanced by three complementary functions (autonomous automatic incident detection algorithm, reliable sample size function, and driver behavior model) and evaluates it in the typical $6{\times}6$ urban grid network with non-recurrent traffic state (traffic incident) with the varying key parameters (traffic flow, communication radio range, and penetration ratio), employing the off-the-shelf microscopic simulation model (VISSIM) under the ideal vehicle communication environment. Simulation outputs indicate that as the three key parameters are increased more participating vehicles are involved for traffic data propagation in the less communication groups at the faster data dissemination speed. Also, participating vehicles saved their travel time by dynamically updating the up-to-date traffic states and searching for the new route. Focusing on the travel time difference of (instant) re-routing vehicles, lower traffic flow cases saved more time than higher traffic flow ones. This is because a relatively small number of vehicles in 300vph case re-route during the most system-efficient time period (the early time of the traffic incident) but more vehicles in 514vph case re-route during less system-efficient time period, even after the incident is resolved. Also, normally re-routings on the network-entering links saved more travel time than any other places inside the network except the case where the direct effect of traffic incident triggers vehicle re-routings during the effective incident time period and the location and direction of the incident link determines the spatial distribution of re-routing vehicles.