Optimizing delivery lead time/inventory placement in a two-stage production/distribution system

Chung

et al. 2013

Multimed Tools Appl

In the recent environmental protection the reverse logistics of the used product is one of the most important research topics. The reverse logistics is the process flow of usedproducts that are collected to be reproduced so that they can be sold again to customers after some processing. We propose a multi-objective hybrid genetic algorithm (mo-hGA) combined with Fuzzy Logic Controller (FLC) for efficiently dealing with multi-objective reverse logistics network (mo-RLN) problem. The aim of this paper is firstly to formulate mo-RLN model, and secondly to optimize it by mo-hGA method proposed with reusable system configuration. In particular two objective functions to be minimized total costs of mo-RLN, (i.e. fixed opening cost, transportation cost and inventory cost) and also minimized delivery tardiness in all periods are considered in the model. We will clear each objective function (i.e. total costs and total delivery tardiness), computational time and number of Pareto solutions with LINGO, pri-awGA (priority-based GA with adaptive weight approach) and mo-hGA proposed with numerical examples. For demonstrating the effectiveness of the proposed model, we evaluate with the numerical examples and simulate it with a bottles distilling/sale company as a case study in Busan, Korea. Keywords Multi-objective reverse logistics network (mo-RLN). Pri-awGA (priority-based GA with adaptive weight approach). Multi-objective hybrid genetic algorithm (mo-hGA). Fuzzy logic controller (FLC)

Section: Delivery Lead Time and Inventorymentioning

confidence: 99%

A multi-objective hybrid genetic algorithm to minimize the total cost and delivery tardiness in a reverse logistics

Chung

et al. 2013

Multimed Tools Appl

“…Finally, a group of researchers explores delivery leadtime commitments and quotations under nonstrategic settings. For example, Barnes-Schuster et al (2006) study a two-level supply chain under full information and normally distributed demand. They provide conditions under which the retailer or supplier should hold the entire inventory.…”

Section: Literature Reviewmentioning

confidence: 99%

Promised Lead-Time Contracts Under Asymmetric Information

Lutze

Özer

2008

Operations Research

We study the important problem of how a supplier should optimally share the consequences of demand uncertainty (i.e., the cost of inventory excesses and shortages) with a retailer in a two-level supply chain facing a finite planning horizon. In particular, we characterize a multiperiod contract form, the promised lead-time contract, that reduces the supplier's risk from demand uncertainty and the retailer's risk from uncertain inventory availability. Under the contract terms, the supplier guarantees on-time delivery of complete orders of any size after the promised lead time. We characterize the optimal promised lead time and the corresponding payments that the supplier should offer to minimize her expected inventory cost, while ensuring the retailer's participation. In such a supply chain, the retailer often holds private information about his shortage cost (or his service level to end customers). Hence, to understand the impact of the promised lead-time contract on the supplier's and the retailer's performance, we study the system under local control with full information and local control with asymmetric information. By comparing the results under these information scenarios to those under a centrally controlled system, we provide insights into stock positioning and inventory risk sharing. We quantify, for example, how much and when the supplier and the retailer overinvest in inventory as compared to the centrally controlled supply chain. We show that the supplier faces more inventory risk when the retailer has private service-level information. We also show that a supplier located closer to the retailer is affected less by information asymmetry. Next, we characterize when the supplier should optimally choose not to sign a promised lead-time contract and consider doing business under other settings. In particular, we establish the optimality of a cutoff level policy. Finally, under both full and asymmetric servicelevel information, we characterize conditions when optimal promised lead times take extreme values of the feasible set, yielding the supplier to assume all or none of the inventory risk-hence the name all-or-nothing solution. We conclude with numerical examples demonstrating our results.

2007 IEEE International Conference on Industrial Engineering and Engineering Management

“…After M passes 9, the total cost values become increasing. Hence, M * =5 and m * = (4,5,5,6,6). According to formula (8), the optimal total cost is TC * = 44968.…”

Section: Solutionmentioning

confidence: 99%

“…Pan [3] and Yang [4] thought a lead time as controllable and studied the optimal lead time decision making. Barnes etc [5] considered a lead time factor when making a facility planning. He studied how to determine the optimal delivery lead time and the resulting location of the system inventory given the supplier facing a known production lead time and the buyer facing a random demand of a known distribution function.…”

Section: Introductionmentioning

confidence: 99%

Planning for supply chain with seasonal variable delivery time

Wang

Liu

Ding

2007

The fluctuation of the seasonal variable delivery time significantly impacts the operating costs of production and distribution systems. This paper finds that a non-linear deliver time function which has a non-zero second derivative can simulate the practical situation more than linear function which has a zero second derivative based on practical data. And this paper focuses on a supply chain in which the retailer requires a small-lot at a fixed interval, the manufacturer sets up a distribution center to satisfy the demand, and the manufacture facility is far from the retailer. A search algorithm for finding the optimal production lot and ordering quantity in a system of non-linear deliver times is proposed. The sensitivity analysis shows that cost savings can be achieved by planning production cycles according to the acceleration of deliver time changes and in-transit costs.