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

Customer-to-customer returns logistics:Can it mitigate the negative impact of online returns?

Customer returns are a major problem for online retailers due to their economic and environmental impact. This paper investigates a new concept for handling online returns: customer-to-customer (C2C) returns logistics. The idea behind the C2C concept is to deliver returned items directly to the next customer, bypassing the retailer's warehouse. To incentivize customers to purchase C2C return items, retailers can promote return items on their webshop with a discount. We build the mathematical models behind the C2C concept to determine how much discount to offer to ensure enough customers are induced to purchase C2C return items and to maximize the retailer's expected total profit. Our first model, the base model (BM), is a customer-based formulation of the problem and provides an easy-to-implement constant-discount-level policy. Our second model formulates the real-world problem as a Markov decision process (MDP). Since our MDP suffers from the curse of dimensionality, we resort to simulation optimization (SO) and reinforcement learning (RL) methods to obtain reasonably good solutions. We apply our methods to data collected from a Dutch fashion retailer. We also provide extensive numerical experiments to claim generality. Our results indicate that the constant-discount-level policy obtained with the BM performs well in terms of expected profit compared to SO and RL. With the C2C concept, significant benefits can be achieved in terms of both expected profit and return rate. Even in cases where the cost-effectiveness of the C2C returns program is not pronounced, the proportion of customer-to-warehouse returns to total demand becomes lower. Therefore, the system can be defined as more environmentally friendly. The C2C concept can help retailers financially address the problem of online returns and meet the growing need for reducing their environmental impact.</p

Customer-to-customer returns logistics:Can it mitigate the negative impact of online returns?

Customer returns are a major problem for online retailers due to their economic and environmental impact. This paper investigates a new concept for handling online returns: customer-to-customer (C2C) returns logistics. The idea behind the C2C concept is to deliver returned items directly to the next customer, bypassing the retailer's warehouse. To incentivize customers to purchase C2C return items, retailers can promote return items on their webshop with a discount. We build the mathematical models behind the C2C concept to determine how much discount to offer to ensure enough customers are induced to purchase C2C return items and to maximize the retailer's expected total profit. Our first model, the base model (BM), is a customer-based formulation of the problem and provides an easy-to-implement constant-discount-level policy. Our second model formulates the real-world problem as a Markov decision process (MDP). Since our MDP suffers from the curse of dimensionality, we resort to simulation optimization (SO) and reinforcement learning (RL) methods to obtain reasonably good solutions. We apply our methods to data collected from a Dutch fashion retailer. We also provide extensive numerical experiments to claim generality. Our results indicate that the constant-discount-level policy obtained with the BM performs well in terms of expected profit compared to SO and RL. With the C2C concept, significant benefits can be achieved in terms of both expected profit and return rate. Even in cases where the cost-effectiveness of the C2C returns program is not pronounced, the proportion of customer-to-warehouse returns to total demand becomes lower. Therefore, the system can be defined as more environmentally friendly. The C2C concept can help retailers financially address the problem of online returns and meet the growing need for reducing their environmental impact.</p

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

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

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.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.CHATENAY MALABRY-Ecole centrale (920192301) / SudocSudocFranceF

Optimizing usage and maintenance decisions for k-out-of-n systems of moving assets

We consider an integrated usage and maintenance optimization problem for a k-out-of-n system pertaining to a moving asset. The k-out-of-n systems are commonly utilized in practice to increase availability, where n denotes the total number of parallel and identical units and k the number of units required to be active for a functional system. Moving assets such as aircraft, ships, and submarines are subject to different operating modes. Operating modes can dictate not only the number of system units that are needed to be active, but also where the moving asset physically is, and under which environmental conditions it operates. We use the intrinsic age concept to model the degradation process. The intrinsic age is analogous to an intrinsic clock which ticks on a different pace in different operating modes. In our problem setting, the number of active units, degradation rates of active and standby units, maintenance costs, and type of economic dependencies are functions of operating modes. In each operating mode, the decision maker should decide on the set of units to activate (usage decision) and the set of units to maintain (maintenance decision). Since the degradation rate differs for active and standby units, the units to be maintained depend on the units that have been activated, and vice versa. In order to minimize maintenance costs, usage and maintenance decisions should be jointly optimized. We formulate this problem as a Markov decision process and provide some structural properties of the optimal policy. Moreover, we assess the performance of usage policies that are commonly implemented for maritime systems. We show that the cost increase resulting from these policies is up to 27% for realistic settings. Our numerical experiments demonstrate the cases in which joint usage and maintenance optimization is more valuable.</p

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

International audienceWe 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