315 research outputs found
Evolutionary multiobjective optimization of the multi-location transshipment problem
We consider a multi-location inventory system where inventory choices at each
location are centrally coordinated. Lateral transshipments are allowed as
recourse actions within the same echelon in the inventory system to reduce
costs and improve service level. However, this transshipment process usually
causes undesirable lead times. In this paper, we propose a multiobjective model
of the multi-location transshipment problem which addresses optimizing three
conflicting objectives: (1) minimizing the aggregate expected cost, (2)
maximizing the expected fill rate, and (3) minimizing the expected
transshipment lead times. We apply an evolutionary multiobjective optimization
approach using the strength Pareto evolutionary algorithm (SPEA2), to
approximate the optimal Pareto front. Simulation with a wide choice of model
parameters shows the different trades-off between the conflicting objectives
Simultaneous Planning of Liner Ship Speed Optimization, Fleet Deployment, Scheduling and Cargo Allocation with Container Transshipment
Due to a substantial growth in the world waterborne trade volumes and drastic
changes in the global climate accounted for CO2 emissions, the shipping
companies need to escalate their operational and energy efficiency. Therefore,
a multi-objective mixed-integer non-linear programming (MINLP) model is
proposed in this study to simultaneously determine the optimal service
schedule, number of vessels in a fleet serving each route, vessel speed between
two ports of call, and flow of cargo considering transshipment operations for
each pair of origin-destination. This MINLP model presents a trade-off between
economic and environmental aspects considering total shipping time and overall
shipping cost as the two conflicting objectives. The shipping cost comprises of
CO2 emission, fuel consumption and several operational costs where fuel
consumption is determined using speed and load. Two efficient evolutionary
algorithms: Nondominated Sorting Genetic Algorithm II (NSGA-II) and Online
Clustering-based Evolutionary Algorithm (OCEA) are applied to attain the
near-optimal solution of the proposed problem. Furthermore, six problem
instances of different sizes are solved using these algorithms to validate the
proposed model.Comment: 28 pages, 10 figure
Multiobjective Coordination Models For Maintenance And Service Parts Inventory Planning And Control
In many equipment-intensive organizations in the manufacturing, service and particularly the defense sectors, service parts inventories constitute a significant source of tactical and operational costs and consume a significant portion of capital investment. For instance, the Defense Logistics Agency manages about 4 million consumable service parts and provides about 93% of all consumable service parts used by the military services. These items required about US50 billion. The US Army Institute of Land Warfare reports that, at the beginning of the 2003 fiscal year, prior to Operation Iraqi Freedom the aviation service parts alone was in excess of US10.5 billion in appropriations spent on purchasing service parts in 2000, the United States Air Force (USAF) continues to report shortages of service parts. The USAF estimates that, if the investment on service parts decreases to about US$5.3 billion, weapons systems availability would range from 73 to 100 percent. Thus, better management of service parts inventories should create opportunities for cost savings caused by the efficient management of these inventories. Unfortunately, service parts belong to a class of inventory that continually makes them difficult to manage. Moreover, it can be said that the general function of service parts inventories is to support maintenance actions; therefore, service parts inventory policies are highly related to the resident maintenance policies. However, the interrelationship between service parts inventory management and maintenance policies is often overlooked, both in practice and in the academic literature, when it comes to optimizing maintenance and service parts inventory policies. Hence, there exists a great divide between maintenance and service parts inventory theory and practice. This research investigation specifically considers the aspect of joint maintenance and service part inventory optimization. We decompose the joint maintenance and service part inventory optimization problem into the supplier s problem and the customer s problem. Long-run expected cost functions for each problem that include the most common maintenance cost parameters and service parts inventory cost parameters are presented. Computational experiments are conducted for a single-supplier two-echelon service parts supply chain configuration varying the number of customers in the network. Lateral transshipments (LTs) of service parts between customers are not allowed. For this configuration, we optimize the cost functions using a traditional, or decoupled, approach, where each supply chain entity optimizes its cost individually, and a joint approach, where the cost objectives of both the supplier and customers are optimized simultaneously. We show that the multiple objective optimization approach outperforms the traditional decoupled optimization approach by generating lower system-wide supply chain network costs. The model formulations are extended by relaxing the assumption of no LTs between customers in the supply chain network. Similar to those for the no LTs configuration, the results for the LTs configuration show that the multiobjective optimization outperforms the decoupled optimization in terms of system-wide cost. Hence, it is economically beneficial to jointly consider all parties within the supply network. Further, we compare the model configurations LTs versus no LTs, and we show that using LTs improves the overall savings of the system. It is observed that the improvement is mostly derived from reduced shortage costs since the equipment downtime is reduced due to the proximity of the supply. The models and results of this research have significant practical implications as they can be used to assist decision-makers to determine when and where to pre-position parts inventories to maximize equipment availability. Furthermore, these models can assist in the preparation of the terms of long-term service agreements and maintenance contracts between original equipment manufacturers and their customers (i.e., equipment owners and/or operators), including determining the equitable allocation of all system-wide cost savings under the agreement
On green routing and scheduling problem
The vehicle routing and scheduling problem has been studied with much
interest within the last four decades. In this paper, some of the existing
literature dealing with routing and scheduling problems with environmental
issues is reviewed, and a description is provided of the problems that have
been investigated and how they are treated using combinatorial optimization
tools
A Review on Quantitative Approaches for Dock Door Assignment in Cross-Docking
Cross docking is a relatively new technique in supply chain operations. It offers limited storage time to maximize the efficiency of goods transshipment. Efficient operation of a cross docking system requires an appropriate coordination of inbound and outbound flows, accurate planning and dynamic scheduling. Â The planning strategies at cross docking terminals, which are receiving growing attention today, are the truck-to-door assignment and destination to door assignment problems. This paper provides a comprehensive literature review of quantitative approaches in dock door assignment problems of cross docking planning. The contributions of this paper are to identify the gap of knowledge in operational levels mainly in dock door assignment and to point out the future research direction in cross docking
Integrating Multiobjective Optimization With The Six Sigma Methodology For Online Process Control
Over the past two decades, the Define-Measure-Analyze-Improve-Control (DMAIC) framework of the Six Sigma methodology and a host of statistical tools have been brought to bear on process improvement efforts in today’s businesses. However, a major challenge of implementing the Six Sigma methodology is maintaining the process improvements and providing real-time performance feedback and control after solutions are implemented, especially in the presence of multiple process performance objectives. The consideration of a multiplicity of objectives in business and process improvement is commonplace and, quite frankly, necessary. However, balancing the collection of objectives is challenging as the objectives are inextricably linked, and, oftentimes, in conflict. Previous studies have reported varied success in enhancing the Six Sigma methodology by integrating optimization methods in order to reduce variability. These studies focus these enhancements primarily within the Improve phase of the Six Sigma methodology, optimizing a single objective. The current research and practice of using the Six Sigma methodology and optimization methods do little to address the real-time feedback and control for online process control in the case of multiple objectives. This research proposes an innovative integrated Six Sigma multiobjective optimization (SSMO) approach for online process control. It integrates the Six Sigma DMAIC framework with a nature-inspired optimization procedure that iteratively perturbs a set of decision variables providing feedback to the online process, eventually converging to a set of tradeoff process configurations that improves and maintains process stability. For proof of concept, the approach is applied to a general business process model – a well-known inventory management model – that is formally defined and specifies various process costs as objective functions. The proposed iv SSMO approach and the business process model are programmed and incorporated into a software platform. Computational experiments are performed using both three sigma (3σ)-based and six sigma (6σ)-based process control, and the results reveal that the proposed SSMO approach performs far better than the traditional approaches in improving the stability of the process. This research investigation shows that the benefits of enhancing the Six Sigma method for multiobjective optimization and for online process control are immense
Adaptive autotuning mathematical approaches for integrated optimization of automated container terminal
With the development of automated container terminals (ACTs), reducing the loading and unloading time of operation and improving the working efficiency and service level have become the key point. Taking into account the actual operation mode of loading and unloading in ACTs, a mixed integer programming model is adopted in this study to minimize the loading and unloading time of ships, which can optimize the integrated scheduling of the gantry cranes (QCs), automated guided vehicles (AGVs), and automated rail-mounted gantries (ARMGs) in automated terminals. Various basic metaheuristic and improved hybrid algorithms were developed to optimize the model, proving the effectiveness of the model to obtain an optimized scheduling scheme by numerical experiments and comparing the different performances of algorithms. The results show that the hybrid GA-PSO algorithm with adaptive autotuning approaches by fuzzy control is superior to other algorithms in terms of solution time and quality, which can effectively solve the problem of integrated scheduling of automated container terminals to improve efficiency.info:eu-repo/semantics/publishedVersio
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