Throwing out food before expiration and still reducing food waste: online vs. offline retail

Online retailers throw out food that has not yet expired. This gives rise to the question whether online retailers generate more food waste than offline retailers, who typically throw out food only after it has expired. We focus on the food waste at the retailer which inherently ensues from the logistical set-up. We first provide a theoretical analysis to establish whether throwing out food before expiration indeed results in an increase in food waste, putting online retailers at a disadvantage compared to offline retailers. We show the relevance of this question by providing a theoretical example, showing an inventory control policy which counter-intuitively results in a decrease in food waste. Nonetheless, we show for well-behaved inventory control policies, including the optimal policy, that food waste increases when food is thrown out before expiration. Next, we compare the food waste of the online retailer with that of an offline retailer in numerical experiments. Note that the online retailer has some advantages over offline retailers as well. Online retailers benefit from full control of order picking, which is instead often done by the consumer in offline retail. Moreover, the online retailer often benefits from the pooling effect, as offline retailers might use multiple stores to satisfy the same demand volume as an online retailer from a single warehouse. Our numerical experiments with a base-stock policy suggests that online retail actually yields less food waste for many products, despite throwing out food before expiration

Integrated optimization of maintenance interventions and spare part selection for a partially observable multi-component system

Advanced technical systems are typically composed of multiple critical components whose failure cause a system failure. Often, it is not technically or economically possible to install sensors dedicated to each component, which means that the exact condition of each component cannot be monitored, but a system level failure or defect can be observed. The service provider then needs to implement a condition based maintenance policy that is based on partial information on the systems condition. Furthermore, when the service provider decides to service the system, (s)he also needs to decide which spare part(s) to bring along in order to avoid emergency shipments and part returns. We model this problem as an infinite horizon partially observable Markov decision process. In a set of numerical experiments, we first compare the optimal policy with preventive and corrective maintenance policies: The optimal policy leads on average to a 28% and 15% cost decrease, respectively. Second, we investigate the value of having full information, i.e., sensors dedicated to each component: This leads on average to a 13% cost decrease compared to the case with partial information. Interestingly, having full information is more valuable for cheaper, less reliable components than for more expensive, more reliable components

Operations research models and methods for safety stock determination: A review

In supply chain inventory management it is generally accepted that safety stocks are a suitable strategy to deal with demand and supply uncertainty aiming to prevent inventory stock-outs. Safety stocks have been the subject of intensive research, typically covering the problems of dimensioning, positioning, managing and placement. Here, we narrow the scope of the discussion to the safety stock dimensioning problem, consisting in determining the proper safety stock level for each product. This paper reports the results of a recent in-depth systematic literature review (SLR) of operations research (OR) models and methods for dimensioning safety stocks. To the best of our knowledge, this is the first systematic review of the application of OR-based approaches to investigate this problem. A set of 95 papers published from 1977 to 2019 has been reviewed to identify the type of model being employed, as well as the modeling techniques and main performance criteria used. At the end, we highlight current literature gaps and discuss potential research directions and trends that may help to guide researchers and practitioners interested in the development of new OR-based approaches for safety stock determination.This work has been supported by FCT – Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020, and by the European Structural and Investment Funds in the FEDER component, through the Operational Competitiveness and Internationalization Program (COMPETE 2020) [Project no. 39479, Funding reference: POCI-01-0247-FEDER-39479]

Optimal spare parts management for vessel maintenance scheduling

Condition-based monitoring is used as part of predictive maintenance to collect real-time information on the healthy status of a vessel engine, which allows for a more accurate estimation of the remaining life of an engine or its parts, as well as providing a warning for a potential failure of an engine part. An engine failure results in delays and down-times in the voyage of a vessel, which translates into additional cost and penalties. This paper studies a spare part management problem for maintenance scheduling of a vessel operating on a given route that is defined by a sequence of port visits. When a warning on part failure is received, the problem decides when and to which port each part should be ordered, where the latter is also the location at which the maintenance operation would be performed. The paper describes a mathematical programming model of the problem, as well as a shortest path dynamic programming formulation for a single part which solves the problem in polynomial time complexity. Simulation results are presented in which the models are tested under different scenarios

Contributions au problème d’optimisation de stocks multi-échelons en utilisant le modèle de service garanti

Many real-world supply chains can be characterised as large and complex multi-echelon systems since they consist of several stages incorporating assembly and distribution processes. A challenge facing such systems is the efficient management of inventory when demand is uncertain, operating costs and customer service requirements are high. This requires specifying the inventory levels at different stages that minimise the total cost and meet target customer service levels. In order to address this problem, researchers proposed the Stochastic-Service Model and the Guaranteed-Service Model (GSM) approaches. These two approaches differ in terms of assumptions with regard to how to address demand variations and service times. This thesis develops several contributions to the GSM based multi-echelon inventory optimisation problem. First of all, we conduct a comprehensive literature review which gives a synthesis of the various GSM work developed so far. Then, we study the impact of some specific assumptions of the GSM such as bounded demand, guaranteed-service times and common review periods. Our numerical analysis shows that the bounded demand assumption may cause a deviation on customer service levels while the guaranteed-service times and common review periods assumptions may result in an increase on the total cost. In real-world supply chains the impact of these assumptions might be significant. Based on the findings presented while investigating the impact of the common review periods assumption, we develop an extension of the GSM that enables to simultaneously optimise the review periods (reorder intervals) and safety stock levels (order-up-to levels) in general acyclic multi-echelon systems. We formulate this problem as a nonlinear integer programming model. Then, we propose a sequential optimisation procedure that enables to obtain near optimal solutions with reasonable computational time. Finally, we focus on the issue of customer service level deviation in the GSM and propose two approaches in order to mitigate this deviation. The numerical study shows that the first approach outperforms the second one in terms of computational time while the second approach provides more accurate solutions in terms of cost. We also present some related issues in decentralised supply chain settings.De nombreuses chaînes logistiques peuvent être caractérisées comme de larges systèmes multi-échelons, car ils se composent souvent de plusieurs étages qui intègrent des activités d'assemblage et de distribution. L’un des enjeux majeurs associé au management de ces systèmes multi-échelons est la gestion efficace de stocks surtout dans des environnements où la demande est incertaine, les coûts de stocks sont importants et les exigences en termes de niveau de service client sont élevées. Cela nécessite en particulier de spécifier les niveaux de stocks aux différents étages afin de minimiser le coût total du système global et de satisfaire les niveaux cibles de service client. Pour faire face à ce problème, deux approches existent dans la littérature; il s’agit du Modèle de Service Stochastique (SSM) et le Modèle de Service Garanti (GSM). Ces deux approches diffèrent en termes d'hypothèses utilisées concernant la façon de gérer les variations de la demande et les temps de service. Cette thèse amène plusieurs contributions au problème d'optimisation de stocks multi-échelons basé sur le GSM. Tout d'abord, nous menons une revue de la littérature internationale qui donne une synthèse des différents travaux réalisés à ce jour. Ensuite, nous étudions l'impact de certaines hypothèses spécifiques du GSM comme la demande bornée, les temps de service garanti et les périodes d’approvisionnement communes. Notre analyse numérique montre que l'hypothèse de demande bornée peut causer une déviation sur les niveaux de service client tandis que les hypothèses de temps de service garanti et de périodes d’approvisionnement communes peuvent entraîner une augmentation du coût total. En pratique, l’impact de ces hypothèses peut être important. En se basant sur les résultats présentés lors de l'analyse de l’hypothèse des périodes d'approvisionnement communes, nous développons une extension du GSM qui permet d'optimiser simultanément les périodes d’approvisionnement (les intervalles de réapprovisionnement) et les niveaux de stocks de sécurité (les niveaux de recomplétement) dans les systèmes multi-échelons acycliques généraux. Nous formulons ce problème comme un modèle de programmation non-linaire en nombres entiers. Ensuite, nous proposons une procédure d'optimisation séquentielle qui permet d'obtenir des solutions proches de l’optimal avec un temps de calcul raisonnable. Enfin, nous nous concentrons sur le problème de déviation de niveau de service client dans le GSM et nous proposons deux approches afin d'atténuer cette déviation. L'étude numérique montre que la première approche est plus performante que la deuxième en termes de temps de calcul tandis que la deuxième approche offre des meilleures solutions en termes de coût. Nous présentons également des problèmes similaires dans les chaînes logistiques décentralisées

Optimising reorder intervals and order-up-to levels in guaranteed service supply chains

We consider the problem of determining the optimal reorder intervals R and order-up-to levels S in a multi-echelon supply chain system where all echelons are assumed to have fixed ordering costs and to operate with a (R, S) policy with stationary nested power-of-two reorder intervals. By using the guaranteed service approach to model the multi-echelon system facing a stochastic demand, we formulate the problem as a deterministic optimisation model in order to simultaneously determine the optimal R and S parameters as well as the guaranteed service times. The model is a non-linear integer programming (NLIP) problem with a non-convex and non-concave objective function including rational and square root terms. Then, we propose a sequential optimisation procedure (SOP) to obtain near-optimal solutions with reasonable computational time. The numerical study demonstrates that for a general acyclic multi-echelon system with randomly generated parameters, the SOP is able to obtain near-optimal solutions of about 0.46% optimality gap in average in a few seconds. Moreover, we propose an improved direct approach using a global optimiser, bounding the decision variables in the NLIP model and considering the SOP solution as an initial solution. Numerical examples illustrate that this reduces significantly the computational time