Modeling an inventory routing problem for perishable products with environmental considerations
Soysal, M. ; Bloemhof, J.M. ; Haijema, R. ; Vorst, J.G.A.J. van der - \ 2015
International Journal of Production Economics 164 (2015). - ISSN 0925-5273 - p. 118 - 133.
service-level constraints - transshipment - heuristics - systems - stock - time
The transition to sustainable food supply chain management has brought new key logistical aims such as reducing food waste and environmental impacts of operations in the supply chain besides the traditional cost minimization objective. Traditional assumptions of constant distribution costs between nodes, unlimited product shelf life and deterministic demand used in the Inventory Routing Problem (IRP) literature restrict the usage of the proposed models in current food logistics systems. From this point of view, our interest in this study is to enhance the traditional models for the IRP to make them more useful for the decision makers in food logistics management. Therefore, we present a multi-period IRP model that includes truck load dependent (and thus route dependent) distribution costs for a comprehensive evaluation of CO2 emission and fuel consumption, perishability, and a service level constraint for meeting uncertain demand. A case study on the fresh tomato distribution operations of a supermarket chain shows the applicability of the model to a real-life problem. Several variations of the model, each differing with respect to the considered aspects, are employed to present the benefits of including perishability and explicit fuel consumption concerns in the model. The results suggest that the proposed integrated model can achieve significant savings in total cost while satisfying the service level requirements and thus offers better support to decision makers.
Constraint-based Local Search for Inventory Control Under Stochastic Demand and Lead Time
Rossi, R. ; Tarim, S.A. ; Bollapragada, R. - \ 2012
INFORMS journal on computing 24 (2012)1. - ISSN 1091-9856 - p. 66 - 80.
service-level constraints - lot-sizing problem - management - policies - systems - model
In this paper, we address the general multiperiod production/inventory problem with nonstationary stochastic demand and supplier lead time under service-level constraints. A replenishment cycle policy is modeled. We propose two hybrid algorithms that blend constraint programming and local search for computing near-optimal policy parameters. Both algorithms rely on a coordinate descent local search strategy; what differs is the way this strategy interacts with the constraint programming solver. These two heuristics are first, compared for small instances against an existing optimal solution method. Second, they are tested and compared with each other in terms of solution quality and run time on a set of larger instances that are intractable for the exact approach. Our numerical experiments show the effectiveness of our methods.
Constraint Programming for Stochastic Inventory Systems under Shortage Cost
Rossi, R. ; Tarim, S.A. ; Hnich, B. ; Prestwich, S. - \ 2012
Annals of Operations Research 195 (2012)1. - ISSN 0254-5330 - p. 49 - 71.
service-level constraints - lot-sizing problem - management - policies
One of the most important policies adopted in inventory control is the replenishment cycle policy. Such a policy provides an effective means of damping planning instability and coping with demand uncertainty. In this paper we develop a constraint programming approach able to compute optimal replenishment cycle policy parameters under non-stationary stochastic demand, ordering, holding and shortage costs. We show how in our model it is possible to exploit the convexity of the cost-function during the search to dynamically compute bounds and perform cost-based filtering. Our computational experience show the effectiveness of our approach. Furthermore, we use the optimal solutions to analyze the quality of the solutions provided by an existing approximate mixed integer programming approach that exploits a piecewise linear approximation for the cost function.
A State Space Augmentation Algorithm for the Replenishment Cycle Inventory Policy
Rossi, R. ; Tarim, S.A. ; Hnich, B. ; Prestwich, S.D. - \ 2011
International Journal of Production Economics 133 (2011)1. - ISSN 0925-5273 - p. 377 - 384.
service-level constraints - shortest-path problem - lot-sizing problem - strategies
In this work we propose an efficient dynamic programming approach for computing replenishment cycle policy parameters under non-stationary stochastic demand and service level constraints. The replenishment cycle policy is a popular inventory control policy typically employed for dampening planning instability. The approach proposed in this work achieves a significant computational efficiency and it can solve any relevant size instance in trivial time. Our method exploits the well known concept of state space relaxation. A filtering procedure and an augmenting procedure for the state space graph are proposed. Starting from a relaxed state space graph our method tries to remove provably suboptimal arcs and states (filtering) and then it tries to efficiently build up (augmenting) a reduced state space graph representing the original problem. Our experimental results show that the filtering procedure and the augmenting procedure often generate a small filtered state space graph, which can be easily processed using dynamic programming in order to produce a solution for the original problem.
Computing the non-stationary replenishment cycle inventory policy under stochastic supplier lead-times
Rossi, R. ; Tarim, S.A. ; Hnich, B. ; Prestwich, S. - \ 2010
International Journal of Production Economics 127 (2010)1. - ISSN 0925-5273 - p. 180 - 189.
service-level constraints - lot-sizing problem - systems - demand - model
In this paper we address the general multi-period production/inventory problem with non-stationary stochastic demand and supplier lead-time under service level constraints. A replenishment cycle policy (Rn,Sn) is modeled, where Rn is the nth replenishment cycle length and Sn is the respective order-up-to-level. We propose a stochastic constraint programming approach for computing the optimal policy parameters. In order to do so, a dedicated global chance-constraint and the respective filtering algorithm that enforce the required service level are presented. Our numerical examples show that a stochastic supplier lead-time significantly affects policy parameters with respect to the case in which the lead-time is assumed to be deterministic or absent