Decision support system and design for reverse logistics

This paper studies the relative positions of some selected elements in relations to flexibility and cost factors in information system and logistics design settings. Moreover, this study investigates the extent to which organizations can relate, today, to the importance of information technology in logistics and design for reverse logistics in their operational planning and activities. The findings show the degree of their in terest and policies for reverse logistics, and the creation of joint efforts to reduce costs and to increase flexibility

Production and Inventory Control of a Single Product Assemble-to-Order System with Multiple Customer Classes

We consider the optimal production and inventory control of an assemble-to-order system with m components, one end-product, and n customer classes. A control policy specifies when to produce each component and, whenever an order is placed, whether or not to satisfy it from on-hand inventory. We formulate the problem as a Markov decision process and characterize the structure of an optimal policy. We show that a base-stock production policy is optimal, but the base-stock level for each component is dynamic and depends on the inventory level of all other components (more specifically, it is nondecreasing). We show that the optimal inventory allocation for each component is a rationing policy with different rationing levels for different demand classes. The rationing levels for each component are dynamic and also nondecreasing in the inventory level of all other components. We compare the performance of the optimal policy to heuristic policies, including the commonly used base-stock policy with fixed base-stock levels, and find them to perform surprisingly well, especially for systems with lost sales.assemble-to-order (ATO) systems, production and inventory control, Markov decision processes, make-to-stock queues

An efficient new heuristic for the hoist scheduling problem

International audienceIn this paper, we study the hoist scheduling problem. The latter is often encountered in electroplating processes where a variety of jobs have to be processed in small quantities and in a very short amount of time. Basically, the problem consists in scheduling the hoist׳s movements in order to achieve two main objectives: Higher productivity and better product quality. In order to achieve these two goals, we first formulate the problem as a Mixed Integer Linear Programming Model. Then, due to the problem complexity, we develop an efficient heuristic procedure to obtain the hoist׳s job processing sequence. Extensive numerical experiments show that the heuristic performs extremely well compared to a lower bound obtained through the mixed linear programming model and gives the optimal makespan for a large number of problem instances. Furthermore, comparison with the best available heuristic in the literature, shows that ODEST always outperforms the heuristic and achieves an improvement (i.e., reduction) of the makespan (hence the throughput of the line) of up to 43%

A Novel Decomposition-Based Method for Solving General-Product Structure Assemble-to-Order Systems

10.1016/j.ejor.2020.03.01

Optimal production and inventory control of multi-class mixed backorder and lost sales demand class models

10.1016/j.ejor.2020.09.009European Journal of Operational Research2911147-16

Optimal control of a continuous-time W-configuration assemble-to-order system

International audienceWe analyze a W-configuration assemble-to-order system with random lead times, random arrival of demand , and lost sales, in continuous time. Specifically, we assume exponentially distributed production and demand inter-arrival times. We formulate the problem as an infinite-horizon Markov decision process. We deviate from the standard approach by first characterizing a region (the recurrent region) of the state space where all properties of the cost function hold. We then characterize the optimal policy within this region. In particular, we show that within the recurrent region components are always produced. We also characterize the optimal component allocation policy which specifies whether an arriving product demand should be fulfilled. Our analysis reveals that the optimal allocation policy is counter-intuitive. For instance, even when one product dominates the other, in terms of lost sale cost and lost sale cost rate (i. e ., demand rate times the lost sale cost), its demand may not have absolute priority over the other product's demand. We also show that the structure of the optimal policy remains the same for systems with batch production, Erlang distributed production times, and non-unitary product demand. Finally, we propose efficient heuristics that can be either used as an approximation to the optimal policy or can be used as a starting policy for the common algorithms that are used to obtain the optimal policy in an effort to reduce their computational time