Throughput Rate of a Two-worker Stochastic Bucket Brigade

Work-sharing in production systems is a modern approach that improves throughput rate. Work is shifted between cross-trained workers in order to better balance the material now in the system. When a serial system is concerned, a common work-sharing approach is the Bucket-Brigade (BB), by which downstream workers sequentially take over items from adjacent upstream work- ers. When the workers are located from slowest-to-fastest and their speeds are deterministic, it is known that the line does not suffer from blockage or starvation, and achieves the maximal theoretical throughput rate (TR). Very little is known in the literature on stochastic self-balancing systems with work-sharing, and on BB in particular. This paper studies the basic BB model of Bartholdi & Eisenstein (1996) under the assumption of stochastic worker speeds. We identify settings in which conclusions that emerge from deterministic analysis fail to hold when speeds are stochastic, in particular relating to worker order assignment as a function of the problem parameters

The Dynamics of Concepts in a Homogeneous Community

entrop

Optimal Real Estate Pricing and Offer Acceptance Strategy

We consider the problem of choosing the best of a set of sequential offers proposed by the market in a house-selling process. During each decision epoch, the seller sets a listing price, observes the offers and decides whether to accept the maximum one or to reject all of them. We model a fixed holding cost, which is the constant marketing cost of searching for buyers, and a variable cost that is proportional to the number of offers received during each epoch. The objective is to maximize the expected revenue. Most previous studies assume a stationary known distribution from which the buyers’ offers are generated and which reflects the market valuation of the house. In contrast, we assume that the number of incoming offers, and the distribution from which each individual offer is generated, are affected by the seller’s listing price (i.e., price-based demand response). Thus, we propose a new approach for the selling policy, which consists of the listing price and the offer acceptance threshold in each period. We derive the seller’s optimal selling policy and apply it to a scenario involving the sale of individual residential properties in Ames (Iowa), which yields results consistent with empirical observations

Batching in Bucket Brigade Order Picking

Order picking is the most labor cost consuming element in warehouse operations. In this paper, we consider an order picking process in a single picking aisle in the forward pick area, consisting of multiple locations (pick faces). The picking is performed by a group of pickers, each characterized by stochastic (forward and backward) walking process and picking times. We assume that the bucket brigade (BB) approach is applied, in a static environment, in which a given set of orders has to be picked. In order to improve the picking process, we suggest a batching procedure, where the objective is to minimize the total picking time, namely, the makespan. The proposed batching approach has two advantages: (1) it decreases the total travel time, since the items can be picked in a reduced number of picking tours as compared with picking each order separately; and (2) it balances the picking load along the picking aisle, consequently reducing the blockage occurrences. We model the problem as a Constraint Programming (CP) formulation, which was shown to be efficient in providing high quality solutions for non-linear models. Small and large scale examples are given to demonstrate the proposed approach, where the former consists of 24 orders, which are picked in five locations (pick faces), and the latter consists of 50 orders, which are picked in 12 locations. The solution obtained by the CP formulation is compared via simulation with an order by order picking and with a naïve batching approach, in which orders are batched in an arbitrary sequence until approaching the available capacity

Batching and Sequencing in Bucket Brigade Systems with Heterogeneous Items

Poster

Production-based pollution versus deforestation: optimal policy with state-independent and-dependent environmental absorption efficiency restoration process

International audienceAn important yet largely unexamined issue is how the interaction between deforestation and pollution affects economic and environmental sustainability.This article seeks to bridge the gap by introducing a dynamic model of pollution accumulation where polluting emissions can be mitigated and the absorption efficiency of pollution sinks can be restored. We assume that emissions are due to a production activity, and we include deforestation both as an additional source of emissions and as a cause of the exhaustion of environmental absorption efficiency. To account for the fact that the switching of natural sinks to a pollution source can be either possible, and in such a case even reversible, or impossible, we consider that restoration efforts can be either independent from or dependent on environmental absorption efficiency, i.e., state-independent versus state-dependent restoration efforts. We determine (i) whether production or deforestation is the most detrimental from environmental and social welfare perspectives, and (ii) how state-dependent restoration process affects pollution accumulation and deforestation policies and the related environmental and social welfare consequences