Modeling Multi-Product Multi-Stage Supply Chain Network Design

Bahrampour, Peyman; Safari, Mansoureh; Taraghdari, M. B.

doi:10.1016/s2212-5671(16)30017-x

Cited by 11 publications

(9 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This is similar to the multi-stage supply chain problem where consumers are not only assigned to a distribution center but distribution centers are also linked to production plants. An example of the multi-stage supply chain is presented in [18] where the author attempts to find a distribution center for each client, as well as which factories deliver to which distribution center. Most related research on the multi-stage supply chain problem [19] [20] all face the same difference from our research; existing research typically assumes knowledge of the user demand patterns which are harder to predict for long-tail services and newly registered services.…”

Section: Multi-stage Selectionmentioning

confidence: 99%

Docker Layer Placement for On-Demand Provisioning of Services on Edge Clouds

Smet

Dhoedt

Simoens

2018

IEEE Trans. Netw. Serv. Manage.

View full text Add to dashboard Cite

Driven by the increasing popularity of the microservice architecture, we see an increase in services with unknown demand pattern located in the edge network. Predeployed instances of such services would be idle most of the time, which is economically infeasible. Also, the finite storage capacity limits the amount of deployed instances we can offer. Instead, we present an on-demand deployment scheme using the Docker platform. In Docker, service images consist of layers, each layer adding specific functionality. This allows different services to reuse layers, avoiding cluttering the storages with redundant replicas. We propose a layer placement method which allows users to connect to a server, retrieve all necessary layers-possibly from multiple locations-and deploy an instance of the requested service within the desired response time. We search for the best layer placement which maximizes the satisfied demand given the storage and delay constraints. We developed an iterative optimization heuristic which is less exhaustive by dividing the global problem in smaller subproblems. Our simulation results show that our heuristic is able to solve the problem with less system resources. Last, we present interesting use-cases to use this approach in real-life scenarios.

show abstract

Section: Multi-stage Selectionmentioning

confidence: 99%

Docker Layer Placement for On-Demand Provisioning of Services on Edge Clouds

Smet

Dhoedt

Simoens

2018

IEEE Trans. Netw. Serv. Manage.

View full text Add to dashboard Cite

show abstract

“…A branch and bound method was applied to solve this problem. Bahrampour et al [15] presented a three-phase multiproduct supply chain network model. An improved genetic algorithm is used to solve the problem, in which its encoding method is based on priority-centered encoding.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The fitness of these initial schedules is first evaluated; then they are improved by genetic algorithm process (i.e., selection, mutation, and crossover mechanisms) until the stopping criteria is met. The encoding method is referred to in [15]. Note that these schedules are only partial schedules on the planning horizon (i.e., a 30-day schedule).…”

Section: Problem Solvingmentioning

confidence: 99%

Rolling Supply Chain Scheduling considering Suppliers, Production, and Delivery Lot-Size

Huang

Lee

2018

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Supply chain management and integration play a key factor in contemporary manufacturing concept. Companies seek to integrate itself within a cooperative and mutual benefiting supply chain. Supply chain scheduling, as an important aspect of supply chain management, highly emphasizes on minimizing stock costs and delivery costs. Most previous researches on supply chain scheduling problems assume make-to-order production, which includes delivery cost in lot-size. This practice simplifies the complexity of the problem. Instead, this research discusses make-to-contract production, where the supply chain has a rolling planning horizon that changes according to contracts. Within a planning horizon, two types of interval are defined. The first is frozen interval, in which the manufacturing decision cannot be changed. The second is free interval, where schedules can be adjusted depending on new contracts. This research aims to build a robust rolling supply management schedule to satisfy customers’ needs, by considering supplier, production, and delivery lot-size simultaneously. The objective is to effectively decide a combination of supplier, production, and delivery lot-size that minimizes total cost consisting of supplier cost, finish good stock cost, and delivery cost. Based on the concept, this study designs a problem-solving process that combines the methods of rolling planning horizon and genetic algorithm. Delivery size (DS), finish good stock (FS), and early delivery cost (ED) are the three methods applied; each will provide a guideline to produce a feasible solution. By further considering the fluctuations in practical needs and performing an overall evaluation, a robust and optimal supply chain scheduling plan can be decided, including the optimal lot-sizes of supplier, production, and delivery. In the effectiveness test which considers 3 types of customer demands and 11 types of company cost structures, the simulated data test results suggest that the proposed methods in this study have excellent performance.

show abstract

“…In this study, to minimize the cost to be the benchmark goal of fitness. Because the purpose of distribution is contrary to the fitness value obtained, then the fitness value is the inverse of the distribution cost as shown in equation (5) and z as the total cost spent referring to equation (1).…”

Section: Calculate the Fitness Functionmentioning

confidence: 99%

“…Increasingly wide distribution area covered by the company, the more complex the distribution in resolving problems. The wide of the range of the distribution process, implemented with a multi-level model where there are several levels of distributors such as distributor center, dealer and wholesaler [1].…”

Section: Introductionmentioning

confidence: 99%

Cost Optimization of Multi-Level Multi-Product Distribution Using An Adaptive Genetic Algorithm

Sarwani

Mahmudy

Naba

2017

JITeCS

View full text Add to dashboard Cite

Abstract. Distribution is the challenging and interesting problem to be solved. Distribution problems have many facets to be resolved because it is too complex problems such as limited multi-level with one product, one-level and multi-product even desirable in terms of cost also has several different versions. In this study is proposed using an adaptive genetic algorithm that proved able to acquire efficient and promising result than the classical genetic algorithm. As the study and the extension of the previous study, this study applies an adaptive genetic algorithm considering the problems of multi-level distribution and combination of various products. This study considers also the fixed cost and variable cost for each product for each level distributor. By using the adaptive genetic algorithm, the complexity of multi-level and multi-product distribution problems can be solved. Based on the cost, the adaptive genetic algorithm produces promising result compared to the existing algorithm

show abstract

Modeling Multi-Product Multi-Stage Supply Chain Network Design

Cited by 11 publications

References 14 publications

Docker Layer Placement for On-Demand Provisioning of Services on Edge Clouds

Docker Layer Placement for On-Demand Provisioning of Services on Edge Clouds

Rolling Supply Chain Scheduling considering Suppliers, Production, and Delivery Lot-Size

Cost Optimization of Multi-Level Multi-Product Distribution Using An Adaptive Genetic Algorithm

Contact Info

Product

Resources

About