22,317 research outputs found
A unit cost adjusting heuristic algorithm for the integrated planning and scheduling of a two-stage supply chain
Purpose: The stable relationship of one-supplier-one-customer is replaced by a dynamic
relationship of multi-supplier-multi-customer in current market gradually, and efficient
scheduling techniques are important tools of the dynamic supply chain relationship establishing
process. This paper studies the optimization of the integrated planning and scheduling problem
of a two-stage supply chain with multiple manufacturers and multiple retailers to obtain a
minimum supply chain operating cost, whose manufacturers have different production
capacities, holding and producing cost rates, transportation costs to retailers.
Design/methodology/approach: As a complex task allocation and scheduling problem, this
paper sets up an INLP model for it and designs a Unit Cost Adjusting (UCA) heuristic
algorithm that adjust the suppliersâ supplying quantity according to their unit costs step by step
to solve the model.
Findings: Relying on the contrasting analysis between the UCA and the Lingo solvers for
optimizing many numerical experiments, results show that the INLP model and the UCA
algorithm can obtain its near optimal solution of the two-stage supply chainâs planning and
scheduling problem within very short CPU time.
Research limitations/implications: The proposed UCA heuristic can easily help managers to
optimizing the two-stage supply chain scheduling problems which doesnât include the delivery time and batch of orders. For two-stage supply chains are the most common form of actual
commercial relationships, so to make some modification and study on the UCA heuristic
should be able to optimize the integrated planning and scheduling problems of a supply chain
with more reality constraints.
Originality/value: This research proposes an innovative UCA heuristic for optimizing the
integrated planning and scheduling problem of two-stage supply chains with the constraints of
suppliersâ production capacity and the ordersâ delivering time, and has a great practical
significance to the dynamic relationship establishment of multi-supplier-multi-customer in
current market.Peer Reviewe
A unit cost adjusting heuristic algorithm for the integrated planning and scheduling of a two-stage supply chain
Purpose: The stable relationship of one-supplier-one-customer is replaced by a dynamic
relationship of multi-supplier-multi-customer in current market gradually, and efficient
scheduling techniques are important tools of the dynamic supply chain relationship establishing
process. This paper studies the optimization of the integrated planning and scheduling problem
of a two-stage supply chain with multiple manufacturers and multiple retailers to obtain a
minimum supply chain operating cost, whose manufacturers have different production
capacities, holding and producing cost rates, transportation costs to retailers.
Design/methodology/approach: As a complex task allocation and scheduling problem, this
paper sets up an INLP model for it and designs a Unit Cost Adjusting (UCA) heuristic
algorithm that adjust the suppliersâ supplying quantity according to their unit costs step by step
to solve the model.
Findings: Relying on the contrasting analysis between the UCA and the Lingo solvers for
optimizing many numerical experiments, results show that the INLP model and the UCA
algorithm can obtain its near optimal solution of the two-stage supply chainâs planning and
scheduling problem within very short CPU time.
Research limitations/implications: The proposed UCA heuristic can easily help managers to
optimizing the two-stage supply chain scheduling problems which doesnât include the delivery time and batch of orders. For two-stage supply chains are the most common form of actual
commercial relationships, so to make some modification and study on the UCA heuristic
should be able to optimize the integrated planning and scheduling problems of a supply chain
with more reality constraints.
Originality/value: This research proposes an innovative UCA heuristic for optimizing the
integrated planning and scheduling problem of two-stage supply chains with the constraints of
suppliersâ production capacity and the ordersâ delivering time, and has a great practical
significance to the dynamic relationship establishment of multi-supplier-multi-customer in
current market.Peer Reviewe
Global supply chains of high value low volume products
Imperial Users onl
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Decision support for build-to-order supply chain management through multiobjective optimization
This paper aims to identify the gaps in decision-making support based on
multiobjective optimization for build-to-order supply chain management (BTOSCM).
To this end, it reviews the literature available on modelling build-to-order
supply chains (BTO-SC) with the focus on adopting multiobjective optimization
(MOO) techniques as a decision support tool. The literature has been classified based
on the nature of the decisions in different part of the supply chain, and the key
decision areas across a typical BTO-SC are discussed in detail. Available software
packages suitable for supporting decision making in BTO supply chains are also
identified and their related solutions are outlined. The gap between the modelling and
optimization techniques developed in the literature and the decision support needed in
practice are highlighted and future research directions to better exploit the decision
support capabilities of MOO are proposed
Recommended from our members
Decision support for build-to-order supply chain management through multiobjective optimization
This is the post-print version of the final paper published in International Journal of Production Economics. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2010 Elsevier B.V.This paper aims to identify the gaps in decision-making support based on multiobjective optimization (MOO) for build-to-order supply chain management (BTO-SCM). To this end, it reviews the literature available on modelling build-to-order supply chains (BTO-SC) with the focus on adopting MOO techniques as a decision support tool. The literature has been classified based on the nature of the decisions in different part of the supply chain, and the key decision areas across a typical BTO-SC are discussed in detail. Available software packages suitable for supporting decision making in BTO supply chains are also identified and their related solutions are outlined. The gap between the modelling and optimization techniques developed in the literature and the decision support needed in practice are highlighted. Future research directions to better exploit the decision support capabilities of MOO are proposed. These include: reformulation of the extant optimization models with a MOO perspective, development of decision supports for interfaces not involving manufacturers, development of scenarios around service-based objectives, development of efficient solution tools, considering the interests of each supply chain party as a separate objective to account for fair treatment of their requirements, and applying the existing methodologies on real-life data sets.Brunel Research Initiative and Enterprise Fund (BRIEF
Strategies for dynamic appointment making by container terminals
We consider a container terminal that has to make appointments with barges dynamically, in real-time, and partly automatic. The challenge for the terminal is to make appointments with only limited knowledge about future arriving barges, and in the view of uncertainty and disturbances, such as uncertain arrival and handling times, as well as cancellations and no-shows. We illustrate this problem using an innovative implementation project which is currently running in the Port of Rotterdam. This project aims to align barge rotations and terminal quay schedules by means of a multi-agent system. In this\ud
paper, we take the perspective of a single terminal that will participate in this planning system, and focus on the decision making capabilities of its intelligent agent. We focus on the question how the terminal operator can optimize, on an operational level, the utilization of its quay resources, while making reliable appointments with barges, i.e., with a guaranteed departure time. We explore two approaches: (i) an analytical approach based on the value of having certain intervals within the schedule and (ii) an approach based on sources of exibility that are naturally available to the terminal. We use simulation to get insight in the benefits of these approaches. We conclude that a major increase in utilization degree could be achieved only by deploying the sources of exibility, without harming the waiting time of barges too much
Models for supply chain negotiation in collaborative relationships
Nowadays, firms are increasingly building collaborative relationships with their partners in order to improve the global performance of the supply chain in which they are involved. Such collaborative relationships require information exchange or share and negotiation. In this paper, we first formalize some practices of collaboration from case studies of the aeronautical area then suggest some models for negotiation, allowing a supply chain member to publish hidden constraints and share risks/costs in order to achieve a win-win situation
On two-echelon inventory systems with Poisson demand and lost sales
We derive approximations for the service levels of two-echelon inventory systems with lost sales and Poisson demand. Our method is simple and accurate for a very broad range of problem instances, including cases with both high and low service levels. In contrast, existing methods only perform well for limited problem settings, or under restrictive assumptions.\u
Energy-Efficient Antenna Selection and Power Allocation for Large-Scale Multiple Antenna Systems with Hybrid Energy Supply
The combination of energy harvesting and large-scale multiple antenna
technologies provides a promising solution for improving the energy efficiency
(EE) by exploiting renewable energy sources and reducing the transmission power
per user and per antenna. However, the introduction of energy harvesting
capabilities into large-scale multiple antenna systems poses many new
challenges for energy-efficient system design due to the intermittent
characteristics of renewable energy sources and limited battery capacity.
Furthermore, the total manufacture cost and the sum power of a large number of
radio frequency (RF) chains can not be ignored, and it would be impractical to
use all the antennas for transmission. In this paper, we propose an
energy-efficient antenna selection and power allocation algorithm to maximize
the EE subject to the constraint of user's quality of service (QoS). An
iterative offline optimization algorithm is proposed to solve the non-convex EE
optimization problem by exploiting the properties of nonlinear fractional
programming. The relationships among maximum EE, selected antenna number,
battery capacity, and EE-SE tradeoff are analyzed and verified through computer
simulations.Comment: IEEE Globecom 2014 Selected Areas in Communications Symposium-Green
Communications and Computing Trac
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