Chaotic migration-based pseudo parallel genetic algorithm and its application in inventory optimization

Considering premature convergence in the searching process of genetic algorithm, a chaotic migration-based pseudo parallel genetic algorithm (CMPPGA) is proposed, which applies the idea of isolated evolution and information exchanging in distributed Parallel Genetic Algorithm by serial program structure to solve optimization problem of low real-time demand. In this algorithm, asynchronic migration of individuals during parallel evolution is guided by a chaotic migration sequence. Information exchanging among sub-populations is ensured to be efficient and sufficient due to that the sequence is ergodic and stochastic. Simulation study of CMPPGA shows its strong global search ability, superiority to standard genetic algorithm and high immunity against premature convergence. According to the practice of raw material supply, an inventory programming model is set up and solved by CMPPGA with satisfactory results returned.

Distribution Inventory Cost Optimization Under Grey and Fuzzy Uncertainty

The grey fuzzy variable was defined for the two fold uncertain parameters combining grey and fuzziness factors. On the basis of the credibility and chance measure of grey fuzzy variables, the distribution center inventory uncertain programming model was presented. The grey fuzzy simulation technology can generate input-output data for the uncertain functions. The neural network trained from the inputoutput data can approximate the uncertain functions. The designed hybrid intelligent algorithm by embedding the trained neural network into genetic algorithm can optimize the general grey fuzzy programming problems. Finally, one numerical example is provided to illustrate the effectiveness of the model and the hybrid intelligent algorithm.

Replenishment policy and inventory optimization for supply-hub with liability period consideration

A replenishment decision-making model for supply-hub is firstly established from the angle of supplier, and optimal replenishment decision of the supplier is analyzed. Then, inventory optimization model for supply-hub is formulated from the angle of the manufacturer, and the optimization algorithm for obtaining optimal inventory levels is given. The result shows that liability period decides the share of the inventory cost between two sides in supply chain. With the increase of liability period, the service level has been quickly reduced even though the manufacturer's cost has been cut down by transferring the inventory cost to the supplier. As to the safety inventory, if the lower bound of components safety inventory increases, the supplier's cost will rise up more slowly than the liability period does, while the service levels increases as the safety inventory's lower bound is raised.

Dynamic Control and Optimization of Capital-Constrained Stochastic Inventory Systems

For most firms,especially the small-and medium-sized ones,the operational decisions are affected by their internal capital and ability to obtain external capital.However,the majority of the current studies on dynamic inventory control ignore the firm’s financial status and financing issues completely.An important question that arises is:what are the dynamic optimal inventory and financing policies for firms with limited capital and limited access to external capital?In this paper,we review some of the latest developments in this area.After a brief review of single period models,we focus on multi-period dynamic control of the firm who aims to optimize its xpected terminal wealth.Two cases are discussed in detail:self-finance and short term finance.In the first case,the firm has to rely on its own capital for all ordering decisions,while in the second,the firm can borrow short term loan from lenders.A detailed characterization of the optimal policy is presented and its managerial insights are discussed.Several possible extensions are suggested.

Reducing Carbon Emission through Container Shipment Consolidation and Optimization

Human’s impact on earth through global warming is more or less an accepted fact.Ocean freight is estimated to contribute 4-5%of global carbon emissions.Many manufacturing companies that transfer ship goods through full container loads found themselves under-utilizing the containers and resulting in higher carbon footprint per volume shipment.One of the reasons is the choice of non-ideal container sizes for their shipments.In this paper,we first provide an Integer Programming model to minimize the companies’shipping carbon footprints by selecting the ideal container sizes appropriate for their shipment volumes.Secondly,we proposed a strategy to minimize the carbon footprint by consolidating the shipments in the same country from multiple domestic locations at a port of loading by road freight,before the international sea shipment.A mixed-Integer Programming model has been developed to determine if one should ship each shipment separately or have shipments consolidated first before being shipped.Consolidation fills up the containers more efficiently that reduces the overall carbon footprint.Computational results using real-world data indicates a significant 13.4%reduction carbon emission when selecting the optimal combinations of different sizes of containers and an additional 12.1%reduction in carbon emission when shipment consolidation is applied.

Collaborative Optimization of Berth Allocation and Yard Storage in Container Terminals

Considering the interaction between the berth and the yard,this paper studies the collaborative optimization problem of berth allocation and yard storage from the point of the ships over a certain planning period.This collaborative optimization problem is formulated as the integer programming,which aims at minimizing the total truck travel distance.And decision variables are the berthing positions for visiting ships and the storage positions for export containers.Meanwhile,this paper demonstrates the complexity of the problem in theory.And the hybrid tabu genetic algorithm is designed to solve the problem to obtain the optimal berth allocation position and export container storage position.For this algorithm,the rule is applied to generate the initial feasible solutions,and the crossover and mutation operation are simultaneously applied to optimize the initial solutions.Finally,this paper discusses two different scenes:the same berth scene and the same ship scene.The influence of two different scenes on truck travel distance is analyzed by different numerical examples.Numerical examples’results show that the collaborative optimization of berth allocation and yard storage can effectively shorten the truck travel distance and improve the efficiency of terminal operation,which provides the decision support for terminal operators.

Simulation-based optimization of control policy on multi-echelon inventory system for fresh agricultural products

As fresh agricultural products are perishable and vulnerable,reducing inventory cost is a strategic target for supply chain enterprises.How to design a reliable multi-echelon inventory control policy is still a great challenge.Therefore,the inventory cost of a three-level fresh agricultural products inventory system was firstly mathematically analyzed.Then,the simulation-based optimization model of the multi-echelon inventory system for fresh agricultural products was proposed by using the Flexsim simulation software and the improved particle swarm optimization algorithm.Finally,the multi-echelon inventory system is simulated based on a large number of survey data.Simulation results demonstrate that the proposed simulation-based optimization model of multi-echelon inventory system for fresh agricultural products can provide decision-making and technical support for the formulation of inventory control policy,and also it shows that the modeling of system simulation is an effective method to solve the problem of complex system.

A PARTIAL ROBUST OPTIMIZATION APPROACH TO INVENTORY MANAGEMENT FOR THE OFFLINE-TO-ONLINE PROBLEM UNDER DIFFERENT SELLING PRICES

This study examines an optimal inventory strategy when a retailer markets a product at different selling prices through a dual-channel supply chain, comprising an online channel and an offiine channel. Using the operating pattern of the offiine-to-online （020） business model, we develop a partial robust optimization （PRO） model. Then, we provide a closed-form solution when only the mean and standard deviation of the online channel demand distribution is known and the offiine channel demand follows a uniform distribution （partial robust）. Specifically, owing to the good structural properties of the solution, we obtain a heuristic ordering formula for the general distribution case （i.e., the offiine channel demand follows a general distribution）. In addition, a series of numerical experiments prove the rationality of our conjecture. Moreover, after comparing our solution with other possible policies, we conclude that the PRO approach improves the performance of incorporating the internet into an existing supply chain and, thus, is able to adjust the level of conservativeness of the solution. Finally, in a degenerated situation, we compare our PRO approach with a combination of information approach. The results show that the PRO approach has more ＂robust＂ performance. As a result, a reasonable trade-off between robustness and performance is achieved.

Comparative research on three-echelon and two-echelon medicine inventory model with positive lead-time

According to the principle of minimizing total cost, the three-echelon optimized medical inventory model with stochastic lead-time and two-echelon optimized medicine inventory model with fixed lead-time are established. The relationship between lead-time and inventory cost is studied by Matlab software. It shows that the variety of lead-time has an important effect on medicine inventory systems. Numerical simulation and sensitivity analysis of two models are presented by Lingo software. Based on analysis, it is concluded that the two-echelon model with lead-time results in inventory cost savings, and keeps the quality of care as reflected in service levels when compared with the three-echelon network structure.

Optimal Multiperiodic Control for Inventory Coupled Systems: A Multifrequency Second-Order Test

A complex autonomous inventory coupled system is considered. It can take, for example, the form of a network of chemical or biochemical reactors, where the inventory interactions perform the recycling of by-products between the subsystems. Because of the flexible subsystems interactions, each of them can be operated with their own periods utilizing advantageously their dynamic properties. A multifrequency second-order test generalizing the p-test for single systems is described. It can be used to decide which kind of the operation (the static one, the periodic one or the multiperiodic one) will intensify the productivity of a complex system. An illustrative example of the multiperiodic optimization of a complex chemical production system is presented.

Research on a Distribution Center Location Model Based on a Particle Swarm Optimization Algorithm

Logistics is supposed to be the important source of profits for the enterprises besides reducing material consumption and improving labor productivity. Transportation costs, distribution center construction costs, ordering costs, safe inventory costs and inventory holding costs are the important parts of the total logistics costs. In this paper, based on the research results of LMRP（ location model of risk pooling） location with fixed construction cost, the LMRPVCC （ location model of risk pooling based on variable construction cost） will be introduced. Applying particle swarm optimization to several computational instances, the authors find the suboptimum solution of the model.

ASSESSMENT AND OPTIMIZATION OF THE PHASE DIAGRAMS AND THERMODYNAMIC DATA OF SOME BINARY SYSTEMS CONTAINING PRASEODYMIUM CHLORIDES

In this paper a critical assessment and optimization of the phase diagrams and thermodynamic properties of the PrCl_3-MCl(M=Li,Na)and PrCl_3-MCl_2(M=Mg,Ca,Sr,Ba) binary systems have been per- formed.The assessed and optimized binary phase diagrams and thermodynamic data with self consistency are a better basis for constructing multicomponent phase diagrams.

Development of a Tool Cost Optimization Model for Stochastic Demand of Machined Products

Cutting tool management in manufacturing firms constitutes an essential element in production cost optimization. In order to optimize the cutting tool stock level while concurrently minimizing production costs, a cost optimization model which considers machining parameters is required. This inclusive modeling consideration is a major step towards achieving effectiveness of cutting tool management policy in manufacturing systems with stochastic driven policies for tool demand. This paper presents a cost optimization model for cutting tools whose utilization level is assumed to be optimized in respect of the machining parameters. The proposed cost model in this research incorporated the effects of diversified machining costs ranging from operational through machining, shortage, holding, material and ordering costs. The machining of parts was assumed to be a single cutting operation. Holt-Winters forecasting technique was used to create a stochastic demand dataset for a test scenario in the production of a high-end automotive part. Some numerical examples used to validate the developed model were implemented to illustrate the optimal machining and tool inventory conditions. Furthermore, a sensitivity analysis was carried out to study the influence of varying production parameters such as: machine uptime, demand and cutting parameters on the overall production cost. The results showed that a desired low level of tool storage and holding costs were obtained at the optimal stock levels. The machining uptime had a significant influence on the total cost while tool life and cutting feed rate were both identified as the most influential cutting variables on the total cost. Furthermore, the cutting speed rate had a marginal effect on both costs and tool life. Other cost variables such as shortage and tool costs had significantly low effect on the overall cost. The output trend showed that the feed rate is the most significant cutting parameter in the machining operation, hence influencing the cost the most. Also, machine uptime and demand significantly influenced the total production cost.

Single-period Two-product Inventory Model with Reorder and Substitution

In market, excess demands for many products can be met by reorder even during one period, and retailers usually adopt substitution strategy for more benefit. Under the retailer＇s substitution strategy and permission of reorder, we develop the profits maximization model for the two-substitutable-product inventory problem with stochastic demands and proportional costs and revenues. We show that the objective function is concave and submodular, and therefore the optimal policy exists. We present the optimal conditions for order quantity and provide some properties of the optimal order quantities. Comparing our model with Netessine and Rudi＇s, we prove that reorder and adoption of the substitution strategy can raise the general profits and adjust down the general stock level.

Robust solutions of Uncertain Capacity inventory control

This paper analyzes the uncertainties of air cargo and applies revenue management to solve the problem of air cargo capacity control.A robust capacity allocation model for a multiple-leg with multiple shipment types is established,which describe uncertainty of these parameters as a number of discrete scenarios,and obtain the optimal allocation with Mutation Particle Swarm Optimization.Simulation experiments show that this method can balance uncertainty of the model effectively and accord with actual situation.

Robust Optimization Model for Resource Allocation of Container Shipping Lines

The operating efficiency of container shipping lines depends on proper resource allocation of container shipping.A deterministic model was developed for shipping lines based on the equilibrium principle.The objective was to optimize the resource allocation for container lines considering ship size,container deployment,and slot allocation.The deterministic model was then expanded to a robust optimization model accounting for the uncertain factors,while ship size was treated as the design variable and slot allocation as the control variable.The effectiveness of the proposed model is demonstrated using a pendulum shipping line as an example.The results indicate that infeasible solutions will increase and the model robustness will be enhanced by an increased penalty coefficient and the solution robustness will be enhanced by increasing the preference coefficient.The optimization model simultaneously considers demand uncertainty,model robustness,and risk preference of the decision maker to agree better with actual practices.

Fuzzy Optimization of Storage Space Allocation in a Container Terminal

A fuzzy optimization model of storage space allocation is proposed,and a rolling-planning method is derived. The model takes the uncertainty of departure time of import containers and arrival time of export containers into account. For each planning horizon,the problem is decomposed into two levels: the first level minimizes the unbalanced workloads among blocks using hybrid intelligence algorithm;based on block workloads allocated in the above level,the second level minimizes the number of blocks to which the same group of import containers are split. Numerical results show that the model reduces workload imbalance,and speeds up the vessel loading and discharging process.

Vertical Pod Autoscaling in Kubernetes for Elastic Container Collaborative Framework

Kubernetes is an open-source container management tool which automates container deployment,container load balancing and container(de)scaling,including Horizontal Pod Autoscaler(HPA),Vertical Pod Autoscaler(VPA).HPA enables flawless operation,interactively scaling the number of resource units,or pods,without downtime.Default Resource Metrics,such as CPU and memory use of host machines and pods,are monitored by Kubernetes.Cloud Computing has emerged as a platform for individuals beside the corporate sector.It provides cost-effective infrastructure,platform and software services in a shared environment.On the other hand,the emergence of industry 4.0 brought new challenges for the adaptability and infusion of cloud computing.As the global work environment is adapting constituents of industry 4.0 in terms of robotics,artificial intelligence and IoT devices,it is becoming eminent that one emerging challenge is collaborative schematics.Provision of such autonomous mechanism that can develop,manage and operationalize digital resources like CoBots to perform tasks in a distributed and collaborative cloud environment for optimized utilization of resources,ensuring schedule completion.Collaborative schematics are also linked with Bigdata management produced by large scale industry 4.0 setups.Different use cases and simulation results showed a significant improvement in Pod CPU utilization,latency,and throughput over Kubernetes environment.

SIMULATION, ANALYSIS AND OPTIMIZATION OF CONTAINER TERMINALS PROCESSES

The freight logistics includes all the processes needed to supply industry,retailers and wholesalers and final customers with goods.Such processes generate a flow of goods that,in the global supply chain,mainly relies on the activities carried out within worldwide container terminals.In this paper,the authors present a simulation model of a real container terminal.After some preliminary analyses,the simulation model is first used with Design of Experiments and Analysis of Variance to investigate the effects of different resources allocations(i.e.,number of forklifts and tractors)and some parameters(i.e.,inter-arrival times,container unloading time)on the container terminal performances in terms of total number of handled containers per day.Then,based on the results achieved through the Design of Experiments and Analysis of Variance,the simulation model is used with genetic algorithms to carry out a range allocation optimization on berth assignment to incoming ships and number of tractors serving each quay crane.The aim of the optimization is the minimization of the average time spent by each ship in the port area(decreasing,as consequence,costs and increasing service level provided to final customers).