Analysis of the single-vehicle cyclic inventory routing problem

Aghezzaf, El‐Houssaine; Zhong, Yan; Raa, Birger; Mateo, Manuel Otero

doi:10.1080/00207721.2011.564321

Cited by 41 publications

(32 citation statements)

References 11 publications

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“…For example, the singlevehicle cyclic IRP (SV-CIRP), whose objective is to obtain a cyclic distribution plan, belongs to the class of IRPs and often appears as a sub-problem in many logistical problems. Aghezzaf et al [28] formulated SV-CIRP as a mixed-integer program and proposed an exact algorithm for its solution after a thorough analysis of the structure of the problem and its features. Vansteenwegen and Mateo [29] presented an iterated local search meta-heuristic that exploits typical characteristics of the SV-CIRP and opportunities to reduce the computation time.…”

Section: Literature Reviewmentioning

confidence: 99%

An Inventory Routing Policy Under Replenishment Lead Time

Wang

Chan

2017

IEEE Trans. Syst. Man Cybern, Syst.

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The inventory routing problem (IRP) arises in vendor-managed inventory systems, which is a combination of vehicle routing and inventory management. Differing from the traditional IRPs in which the time consumption in transportation is often ignored, in this paper, we take the time consumption into consideration, which brings the lead-time to replenishment. In this situation, the lead-time for replenishment depends on the routing decisions made by the vendor. Consequently, IRPs become more interesting because decisions on the replenishment quantity and routing depend more tightly on each other. The general case of the IRP with replenishment lead-time (i.e., one vendor and N retailers) is modeled mathematically. To solve the problem, first a simple case (i.e., one vendor and two retailers) is analyzed to obtain the theoretical optimal policy. By proving the K-convexity of the objective function, we confirm that the structure of the optimal replenishment and routing policy is of switching curve type. In this policy, the state space, which is composed of the inventory positions of the two retailers, is divided into several domains; and for inventory positions in each domain, there exists an optimal order-upto level. This structure reveals, for managerial insight, that when lead-time is considered, the current routing decision is not independent of the previous one. Second, since the optimal policy is difficult to realize in practice, a myopic policy that is easier to implement is proposed and numerical experiments are conducted to examine the near-optimal performance of the myopic policy. Finally, the myopic policy is extended to a realistic-size IRP with replenishment lead-time (i.e., the IRP for the case of one vendor and multiple retailers) and a numerical example is provided to indicate the feasibility of the policy.

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Section: Literature Reviewmentioning

confidence: 99%

An Inventory Routing Policy Under Replenishment Lead Time

Wang

Chan

2017

IEEE Trans. Syst. Man Cybern, Syst.

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“…The latest algorithm is a successive local enumeration (SLE) method presented by Zhu et al 2011 Pholdee and S. Bureerat This paper presents an alternative but efficient OLHS technique. The method is based on solving a sequencing optimisation problem (SOP) where its best-known problem is the travelling salesman problem (Anbuudayasankar, Ganesh, Lenny Koh, and Mohandas 2010;Aghezzaf, Zhong, Raa, and Mateo 2011;Chen, Feng, and Soon Ong 2011). Simulated annealing (SA) was used to solve the problem in this work.…”

Section: Introductionmentioning

confidence: 99%

An efficient optimum Latin hypercube sampling technique based on sequencing optimisation using simulated annealing

Pholdee

Bureerat

2013

International Journal of Systems Science

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This paper proposes a new optimal Latin hypercube sampling method (OLHS) for design of a computer experiment. The new method is based on solving sequencing and continuous optimisation using simulated annealing. There are two sets of design variables used in the optimisation process: sequencing and real number variables. The special mutation operator is developed to deal with such design variables. The performance of the proposed numerical strategy is tested and compared with three established OLHS methods, namely genetic algorithm (GA), enhanced stochastic evolutionary algorithm (ESEA) and successive local enumeration (SLE). Based on 30 test problems with various design dimensions and numbers of sampling points, the proposed method gives the best results. The method can generate an optimum set of sampling points within reasonable computing time; therefore, it can be considered as a powerful tool for design of computer experiments.

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“…The inventory routing problem (IRP) has been extensively investigated in the last three decades. Researchers have tackled this problem from different points of view and implemented it in many industries . In IRP, a fleet of vehicles is planned to visit retailer(s) during each period from a central depot (or several decentralized warehouses).…”

Section: Introductionmentioning

confidence: 99%

Managing inventory and service levels in a safety stock‐based inventory routing system with stochastic retailer demands

Yadollahi

Aghezzaf

Raa

2017

Appl Stoch Models Bus & Ind

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The inherent uncertainty in supply chain systems compels managers to be more perceptive to the stochastic nature of the systems' major parameters, such as suppliers' reliability, retailers' demands, and facility production capacities. To deal with the uncertainty inherent to the parameters of the stochastic supply chain optimization problems and to determine optimal or close to optimal policies, many approximate deterministic equivalent models are proposed. In this paper, we consider the stochastic periodic inventory routing problem modeled as chance-constrained optimization problem. We then propose a safety stock-based deterministic optimization model to determine near-optimal solutions to this chance-constrained optimization problem. We investigate the issue of adequately setting safety stocks at the supplier's warehouse and at the retailers so that the promised service levels to the retailers are guaranteed, while distribution costs as well as inventory throughout the system are optimized. The proposed deterministic models strive to optimize the safety stock levels in line with the planned service levels at the retailers. Different safety stock models are investigated and analyzed, and the results are illustrated on two comprehensively worked out cases. We conclude this analysis with some insights on how safety stocks are to be determined, allocated, and coordinated in stochastic periodic inventory routing problem. Copyright

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Analysis of the single-vehicle cyclic inventory routing problem

Cited by 41 publications

References 11 publications

An Inventory Routing Policy Under Replenishment Lead Time

An Inventory Routing Policy Under Replenishment Lead Time

An efficient optimum Latin hypercube sampling technique based on sequencing optimisation using simulated annealing

Managing inventory and service levels in a safety stock‐based inventory routing system with stochastic retailer demands

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