A multi-objective healthcare inventory routing problem; a fuzzy possibilistic approach

In the context of a supply chain for the animal‐feed industry, this paper focuses on optimizing replenishment strategies for silos in multiple farms. Assuming that a supply chain is essentially a value chain, our work aims at narrowing this chasm and putting analytics into practice by identifying and quantifying improvements on specific stages of an animal‐feed supply chain. Motivated by a real‐life case, the paper analyses a rich multi‐period inventory routing problem with homogeneous fleet, stochastic demands, and maximum route length. After describing the problem and reviewing the related literature, we introduce a reactive heuristic, which is then extended into a biased‐randomized simheuristic. Our reactive approach is validated and tested using a series of adapted instances to explore the gap between the solutions it provides and the ones generated by existing nonreactive approaches.

show abstract

“…(), jointly with demand uncertainty, estimating

{CO}_{2}

emissions while planning the routes. A fuzzy probabilistic approach is proposed by Niakan and Rahimi (), dealing with the minimization of

{CO}_{2}

emissions in an IRP involving medical distribution. Rahim et al.…”

Section: Literature Reviewmentioning

confidence: 99%

A reactive simheuristic using online data for a real‐life inventory routing problem with stochastic demands

Raba

Estrada‐Moreno

Panadero

et al. 2020

Int Trans Operational Res

View full text Add to dashboard Cite

show abstract

“…() include environmental concerns in their solution for the IRP with demand uncertainty by estimating CO 2 emissions in the route planning process. Greenhouse gas emissions are also minimized in the work of Niakan and Rahimi (), who propose a fuzzy possibilistic approach to a multiobjective IRP for medical drug distribution. Rahim et al.…”

Section: Literature Reviewmentioning

confidence: 99%

A variable neighborhood search simheuristic for the multiperiod inventory routing problem with stochastic demands

Gruler

Panadero

Armas

et al. 2018

Int Trans Operational Res

View full text Add to dashboard Cite

The inventory routing problem (IRP) combines inventory management and delivery route‐planning decisions. This work presents a simheuristic approach that integrates Monte Carlo simulation within a variable neighborhood search (VNS) framework to solve the multiperiod IRP with stochastic customer demands. In this realistic variant of the problem, our goal is to establish the optimal refill policies for each customer–period combination, that is, those individual refill policies that minimize the total expected cost over the periods. This cost is the aggregation of both expected inventory and routing costs. Our simheuristic algorithm allows to consider the inventory changes between periods generated by the realization of the random demands in each period, which have an impact on the quantities to be delivered in the next period and, therefore, on the associated routing plans. A range of computational experiments are carried out in order to illustrate the potential of our simulation–optimization approach.

show abstract

“…Through a simulation experiment, they show that a simple recourse policy is sufficient to provide a reliable and cost-efficient blood supply. Niakan and Rahimi (2015) and Shi et al (2017) study the problem of delivering drugs with uncertain demands to patient homes. Both articles apply fuzzy programming approaches to the problem and report the added value of incorporating uncertainty into the model.…”

Section: Health Care Routing Problemsmentioning

confidence: 99%

A unified framework for rich routing problems with stochastic demands

Markov

Bierlaire

Cordeau

et al. 2018

Transportation Research Part B: Methodological

View full text Add to dashboard Cite

We introduce a unified framework for rich vehicle and inventory routing problems with complex physical and temporal constraints. Demands are stochastic, can be non-stationary, and are forecast using any model that provides the expected demands and their error term distribution, which can be any theoretical or empirical distribution. We offer a detailed discussion on the modeling of demand stochasticity, focusing on the probabilities and cost effects of undesirable events, such as stock-outs, breakdowns and route failures, and their associated recourse actions. Tractability is achieved through the ability to pre-compute or at least partially pre-process the stochastic information, which is possible under mild assumptions for a general inventory policy. We integrate the stochastic aspect into a mixed integer non-linear program, illustrate applications to various problem classes, and show how to model specific problems through the lens of inventory routing. The case study is based on two sets of realistic instances, representing a waste collection inventory routing problem and a facility maintenance problem, respectively. We analyze the effects of our assumptions on modeling realism and tractability, and demonstrate that our framework significantly outperforms deterministic policies in its ability to limit the number of undesirable events for the same routing cost.

show abstract

A multi-objective healthcare inventory routing problem; a fuzzy possibilistic approach

Cited by 90 publications

References 58 publications

A reactive simheuristic using online data for a real‐life inventory routing problem with stochastic demands

A reactive simheuristic using online data for a real‐life inventory routing problem with stochastic demands

A variable neighborhood search simheuristic for the multiperiod inventory routing problem with stochastic demands

A unified framework for rich routing problems with stochastic demands

Contact Info

Product

Resources

About