Hybrid Make-To-Stock (MTS)-Make-To-Order (MTO) manufacturing is a well known policy that captures the benefits of both MTS and MTO policies. This manufacturing policy is adopted by many manufacturing firms because it allows for production based on customer specifications while keeping short response times. We study a hybrid MTS-MTO manufacturing system which consists of two processing stages and an intermediate buffer between these two stages. We propose two separate scenarios for ordering and replenishment of components from the first stage which will give more realistic guidance for practitioners. The first scenario is batching customer orders before being released to the first stage. The second scenario is batch replenishment of common components from the first stage. Most existing MTS-MTO models focus on one-for-one ordering and replenishment strategies. We enhance these models by introducing a batch ordering policy to account for economies of scale in ordering when there is an ordering cost associated with each order placed for common components. We use queueing theory to model the system behavior and use the matrix-geometric method to evaluate system performance under the new ordering policy. Afterwards, we develop an optimization model with the objective to minimize the system overall costs. The purpose of our optimization model is to find the optimal intermediate buffer size and the optimal order quantity for the system. In the second scenario, we introduce the batch replenishment policy from stage 1. This policy is suitable when stage 1 and stage 2 are physically distant and there is a shipping cost incurred when components are transferred from stage 1 to stage 2. The decision variables in this model are the intermediate buffer size and the shipping quantity. We show that the base stock policy is sub-optimal when there is an ordering cost incurred for ordering components. The savings from adopting the batch ordering policy are high and the response time for most customer orders is not affected. When there are shipping costs and shipping time between the two stages, we show that the right selection of the system decision variables can have a large impact on the total cost incurred by the system.

Hybrid Make-To-Stock (MTS)-Make-To-Order (MTO) manufacturing is a well known policy that captures the benefits of both MTS and MTO policies. This manufacturing policy is adopted by many manufacturing firms because it allows for production based on customer specifications while keeping short response times. We study a hybrid MTS-MTO manufacturing system which consists of two processing stages and an intermediate buffer between these two stages. We propose two separate scenarios for ordering and replenishment of components from the first stage which will give more realistic guidance for practitioners. The first scenario is batching customer orders before being released to the first stage. The second scenario is batch replenishment of common components from the first stage. Most existing MTS-MTO models focus on one-for-one ordering and replenishment strategies. We enhance these models by introducing a batch ordering policy to account for economies of scale in ordering when there is an ordering cost associated with each order placed for common components. We use queueing theory to model the system behavior and use the matrix-geometric method to evaluate system performance under the new ordering policy. Afterwards, we develop an optimization model with the objective to minimize the system overall costs. The purpose of our optimization model is to find the optimal intermediate buffer size and the optimal order quantity for the system. In the second scenario, we introduce the batch replenishment policy from stage 1. This policy is suitable when stage 1 and stage 2 are physically distant and there is a shipping cost incurred when components are transferred from stage 1 to stage 2. The decision variables in this model are the intermediate buffer size and the shipping quantity. We show that the base stock policy is sub-optimal when there is an ordering cost incurred for ordering components. The savings from adopting the batch ordering policy are high and the response time for most customer orders is not affected. When there are shipping costs and shipping time between the two stages, we show that the right selection of the system decision variables can have a large impact on the total cost incurred by the system.

In this dissertation, we consider a multi-item two-echelon inventory distribution system in which the central warehouse operates with (Q, R) policy, and each local warehouse implements base-stock policy. The objective is to find the policy parameters minimizing the relevant system-wide costs subject to an aggregate mean response time constraint at each facility. We first propose an exact solution procedure based on a branch-and-price algorithm to find the relevant policy parameters of the system considered. Then, we propose four alternative heuristics to find the optimal or near-optimal policy parameters of large practical-size systems. The first heuristic, which we call the Lagrangian heuristic, is based on the simultaneous approach and relies on the integration of a column generation method and a greedy algorithm. The other three heuristics are based on the sequential approach, in which first the order quantities are determined using a batch size heuristic, then the reorder levels at the central warehouse and the basestock levels at the local warehouses are determined through the same method used for the Lagrangian heuristic. We also propose a lower bound for the system-wide cost. Later, we extend our study to compound Poisson demand. The performance of the Lagrangian heuristic is found to be extremely well and improves even further as the number of parts increases. Also the computational requirement of the heuristic is quite tolerable. This makes the heuristic very promising for large practical industry-size problems. The performance of the sequential heuristics is also satisfactory, but not as much as the Lagrangian heuristic.

Nous nous intéressons au Problème de Dimensionnement de Lots mono-produit (PDL) dans une chaîne logistique composée d'un détaillant et d'un fournisseur en y intégrant le contrat buyback et l'approvisionnement par batch. L'objectif est de déterminer un plan d'approvisionnement pour le détaillant pour satisfaire ses demandes déterministes sur un horizon fini, tout en minimisant ses coûts d'approvisionnement et de stockage. Concernant le coût d'approvisionnement, nous supposons deux structures différentes : FTL (Full Truck Load) et OFB (Only Full Batch). Trois types de contrat buyback sont étudiés : avec des périodes de retour fixes, avec une limite de temps sur les retours, et avec des retours uniquement dans les périodes d'approvisionnement. Chaque contrat est caractérisé par un pourcentage de retour maximal qui peut être égal à 100% (retour total) ou inférieur à 100% (retour partiel). Pour le PDL sous le contrat buyback avec des périodes de retour fixes, nous supposons le cas de ventes perdues (lost sales). En outre, un autre concept ajouté dans les PDL sous les trois types de contrat buyback réside dans le fait que le détaillant peut jeter la quantité invendue et non retournée au fournisseur, appelé mise au rebut (disposal). Nous avons modélisé ces différentes extensions du PDL par des Programmes Linéaires en Nombres Entiers (PLNE). Nous avons ensuite développé des algorithmes exacts polynomiaux de programmation dynamique pour certaines extensions, et montré la NP-difficulté pour d'autres. Pour chaque problème résolu en temps polynomial, nous avons comparé l'efficacité et les limites de l'algorithme proposé avec celles des quatre formulations en PLNE. Nous avons également proposé des modèles mathématiques pour les PDL sous d'autres types de contrats de réservation de capacité dans le cas déterministe à multi-périodes. / We study the single-item Lot Sizing Problem (LSP) in a supply chain composed of a retailer and a supplier by integrating the buyback contract and the batch ordering. The purpose is to determine a replenishment planning for the retailer to satisfy his deterministic demands over a finite horizon, while minimizing the procurement and inventory costs. Regarding the procurement cost, we assume two different structures: FTL (Full Truck Load) and OFB (Only Full Batch). We consider three types of buyback contract: with fixed return periods, with a time limit on returns, and with returns permitted only in procurement periods. Each contract is characterized by the maximum return percentage being either equal to 100% (full return) or less than 100% (partial return). For the LSP under the buyback contract with fixed return periods, we assume the concept of lost sales. Another concept considered in the LSP's under the three types of buyback contract is the disposal of the unsold and unreturned quantities. We model these different LSP extensions as a Mixed Integer Linear Program (MILP). Thereafter, we develop exact polynomial time dynamic programming algorithms for some extensions and show the NP-hardness of others. For each problem solved in polynomial time, we compare the efficiency and the limits of the proposed algorithm with those of four MILP formulations by performing different tests. Finally, we propose mathematical models for the LSP's under other types of the capacity reservation contract in the deterministic and multi-period case.