Earticle

크로스도킹이란 물류와 분배의 개념으로서 창고나 분배 센터에서 물품이 재고로 남겨짐이 없이 곧바로 하역 창구에서 적재 창구로 이동되어지는 것을 말한다 창고시설 이나 운영 조건이나 운영 전략에 의해서 다양한 크로스도킹 모델이 생성될 수 있다. 본 연구에서 고려되어지는 크로스도킹 모델은 하나의 독립된 하역 창구와 독립된 적재 창구를 가정하고 있다. 또한 하역 트럭에 적재되어 있는 물품이나 적재 트럭에 실려질 물품의 수와 종류는 사전에 알려져 있다는 것을 가정한다. 또한 고려되어진 창고나 분배창구는 단지 하나의 하역 창구와 하나의 적재 창구를 가지고 있다고 가정된다. 본 연구의 목적은 고려되어진 크로스도킹 시스템의 총 운영 시간을 최소화하기 위한 하역 트럭과 적재 트럭의 일정 계획을 찾는데 있다.

Cross docking is a material handling and distribution concept in which products move directly from receiving dock to shipping dock, without being stored in a warehouse or distribution center. Depending on the facility and operating conditions or strategies employed, it is possible to generate various cross docking scenarios or models. The cross docking model, which is studied in this research, assumes there are a separate receiving dock and a separate shipping dock. It is also assumed that the products contained in a receiving truck and the products needed for a shipping truck are known in advance. Furthermore, the study is restricted to scenarios where there is only one receiving dock and only one shipping dock at the warehouse. The research objective is to find the best truck spotting sequence for both receiving and shipping trucks to minimize total operation time (i.e., the makespan) of the cross docking system.

일정계획은 오프라인(offline) 일정계획과 온라인(online) 일정계획으로 구분할 수 있고, 본 논문은 온 라인 상황에서 병렬설비의 주기적 일정계획 수립문제를 다룬다. 도착시간(ready time)이 다른 여러 작업들에 대해 makespan을 최소화하기 위한 작업 일정계획 알고리즘 개발이 목적이다. 이를 위해 각 설비에서의 작업 처리 순서는 ERD(Earliest Ready Date) 규칙에 따른 순서가 최적임을 밝혔다. 각 설비 별 배정 작업도 결정 해야하는 병렬설비 문제를 위해서는 혼합정수계획모형(MIP)을 이용하는 알고리즘을 제시하였다. 개발한 알고 리즘의 유용성과 성능분석을 위해 수치 예를 활용하여 오프라인 일정계획과 비교하였다. 비교분석 결과, 오프 라인 일정계획에 비해 매우 빠른 시간에 일정계획을 수립할 수 있음을 보였고, 주기시간의 감소를 통한 makespan의 단축 가능성을 보였다. 본 논문의 주기적 일정계획 방법은 계획수립을 위한 시간이 매우 작으므 로, 설비 및 작업의 수가 많은 온라인 환경에서도 활용할 수 있다. 더불어서 스마트공장이나 블록체인 플랫폼 에서의 작업일정 수행을 위해 활용될 수 있을 것으로 기대한다.

Scheduling problems can be classified into offline and online ones. This paper considers an online scheduling problem to minimize makespan on the identical parallel machines. For dynamically arrived jobs with their ready times, we show that the sequencing order according to the ERD (Earliest Ready Date) rule is optimal to minimize makespan. This paper suggests an algorithm by using the MIP(Mixed Integer Programming) formulation periodically to find a good periodic schedule and evaluates the required computational time and resulted makespan of the algorithm. The comparition with an offline scheduling shows our algorithm makes the schedule very fast and the makespan can be reduced as the period time reduction, so we can conclude that our algorithm is useful for scheduling the jobs under online environment even though the number of jobs and machines is large. We expect that the algorithm is invaluable one to find good schedules for the smart factory and online scheduler using the blockchain mechanism.

This paper considers a coordinated scheduling problem between multi-suppliers and an manufacture. When the supplier has insufficient inventory to meet the manufacture's order, the supplier may use the expedited production and the expedited transportation. In this case, we consider a scheduling problem to minimize the total cost of suppliers and manufacture. We suggest an population management genetic algorithm with local search and crossover (GALPC). By the computational experiments comparing with general genetic algorithm, the objective value of GALPC is reduced by 8% and the calculation time of GALPC is reduced by 70%.

Research the Incident Vehicle Routing Problem with Soft Time-windows (IVRP-STW). Associated logistics scheduling problem with soft time windows using the logistic function optimization method to improve the chaotic particle algorithm, compared with the GA and the standard PSO algorithm. Simulation results show that such optimization method can effectively improve the global search of the particles in the particle swarm and the ability of local search is effective in resolving such problems.

Study the problem of logistic scheduling problem with fuzzy time window, construct mathematical model, and propose double objective function method. In the solution process, for the feature of the double objective functions, use the phased. In the first phase, use the chaos PSO to get the optimal solution. In the second phase, use simulated annealing algorithm and the preliminary solutions got from the first phase to solute the objective function.

In this study, we consider the transportation problem in port container terminals. It aims to determine positions in yards to place the containers at the adequate times. The containers on ship must be unloaded one by one from top to bottom, and placed in the main yard in order to reduce additional cost required for unnecessary unloading when getting out by customer with given timetable. The cost for transportation at container terminals could be reduced by a new approach in scheduling: move the containers from ship and stack them onto main yard that minimizes cost of yard crane operation when unloading for customer.

This paper presents a modified cuckoo search algorithm (MCSA) for solving short-term cascaded hydrothermal scheduling (ST-CHTS) problem. The short-term cascaded hydrothermal scheduling is to determine the optimal operation for thermal plants and a cascaded reservoir system while satisfying all constraints including electrical constraints of both hydro and thermal plants and hydraulic constraints of reservoirs. The MCSA has been developed by modifying the search strategy via Lévy flights to improve the performance of the conventional cuckoo search algorithm. The proposed method has been widely and successfully applied to many optimization problems in engineering fields; however, this is first time employed to search for the optimal solution of the ST-CHTS problem. The proposed method has been tested on two systems where thermal plants with nonconvex fuel cost function and a cascaded reservoir system are taken into account. The result comparison from the MCSA compared to other methods reported in the literature has revealed that the proposed MCSA is very efficient for solving the ST-CHTS problem.

A profit-maximizing economic lot scheduling problem with shelf life items was researched. This paper studies the profit-maximizing economic lot-sizing problem (PELSP), in which the price of the product is a function of demand rate, hence the demand rate for a variable rather than constant, in this paper we use the basic period approach to maximize the profit. A mathematical model was given, and also a numerical illustration and sensitive analysis of the main variables.

The Job-Shop Scheduling Problem (JSSP) is a well-known and one of the challenging combinatorial optimization problems and falls in the NP-complete problem class. This paper presents an algorithm based on integrating Genetic Algorithms and Simulated Annealing methods to solve the Job Shop Scheduling problem. The procedure is an approximation algorithm for the optimization problem i.e. obtaining the minimum makespan in a job shop. The proposed algorithm is based on Genetic algorithm and simulated annealing. SA is an iterative well known improvement to combinatorial optimization problems. The procedure considers the acceptance of cost-increasing solutions with a nonzero probability to overcome the local minima. The problem studied in this research paper moves around the allocation of different operation to the machine and sequencing of those operations under some specific sequence constraint.

The alongside replenishment scheduling problem with time constraint determining the partition of the ships, the order of replenishment and the allocation of time to the ships at the same time is analyzed. It is equivalent to a multi-stage flow shop scheduling problem with the object of maximizing the effectiveness value of ship fleet. The problem solving process is divided into three steps, and based on the analysis of the three steps, a heuristic algorithm is proposed. The algorithm firstly considers the time allocated to each ship, and then sequences the ships by heuristic rule combining greed with insertion, finally determines the ships partitioning to the port and standard side. Emulating example with different problems’ scale and time constraints shows that the proposed heuristic algorithm is superior to some other algorithms.

Flexible Job shop scheduling is very important in production management and combinatorial optimization. It is NP-hard problem and consists of two sub-problems: sequencing and assignment. Multiobjective Flexible Job-Shop Scheduling Problems (MFJSSP) is formulated as three-objective problem which minimizes completion time (makespan), critical machine workload and total work load of all machines. In this paper a Multiobjective Artificial Immune Algorithm (MAIA) for FJSSP is presented. The proposed algorithm increases the speed of convergence and diversity of population. Kacem and Bradimart data are used to evaluate the effectiveness of MAIA. The experimental results show a better performance in comparison to other approaches.

In this application of artificial intelligence to a real-world problem, the constrained scheduling of employee resourcing for a mall type shop is solved by means of a genetic algorithm. hromosomes encode a one-week schedule and a constraint matrix handles all requirements for the population. The genetic operators are purposely designed to preserve all constraints and the objective function assures an imposed coverage, this is for people on both sections of the mall. The results demonstrate that the genetic algorithm approach can provide acceptable solutions to this type of employee scheduling problem with constrains.

This paper present three versions of Cuckoo Search Algorithm (CSA) including conventional Cuckoo Search Algorithm (CSA), modified CSA (MCSA) and adaptive CSA (ACSA) for solving the fixed head short-term hydrothermal scheduling (ST-HTS) problem where the reservoir volume constraints and nonconvex fuel cost function of thermal unit as well as the power losses in transmission line are taken into account. Among the applied methods, ACSA is first developed in the study by performing two modifications on second new solution generation via the action of an alien egg to be abandoned. In the ACSA, all initial solutions or all solutions at the end of the previous iteration are evaluated and sorted into two kinds of solution, good solutions with lower fitness function and bad solutions with higher fitness function. The implementation of the first new solution generation first via Lévy flights in the ACSA is carried out similarly to that in MCSA. However, at the second new solution generation the ACSA evaluates the current solutions to choose the best one and use the information of the best one with a random solution to generate the second new solutions via the action of an alien egg to be abandoned. In addition, the probability of an alien egg discovery is considered an adaptive variable, which is set to the largest value at the beginning and decreased as the iteration is increased. Due to the adaptive value of the parameter, the ACSA can search an optimal solution but the trial runs are significantly decreased compared to CSA and MCSA. The performance of the ACSA is validated by testing on two systems and comparing with CSA, MCSA and other existing methods available in the paper.

Advances in the development of electric vehicles along with policy incentives will see a wider uptake of this technology in the transport sector in the coming years. However, the widespread adoption of electric vehicles will add a substantial energy load to power grids. As a result, many technical problems related to the impact of this technology on the power grid need to be addressed, especially the management and allocation of the energy to the plug-in hybrid electric vehicles (PHEVs), electric vehicles (PEVs). In this paper, we formulated the optimal power allocation to PHEVs/PEVs for the PHEVs/PEVs charging stations scheduling problem as a nonlinear resource allocation continuous problem. We used pegging algorithm to solve the optimal power allocation to the PHEVs/PEVs. A mathematical framework for the objective function (i.e., minimizing the average depth of discharge (DoD) at the next time step) was also given. The authors characterized the performance of optimal power allocation to PHEVs/PEVs problem and pegging algorithm using MATLAB simulation, and compared it with other charging methods.

We applied genetic algorithm to nurse scheduling problem. For time complexity problem of genetic algorithm, we suggested efficient operators using a cost bit matrix of which each cell indicates any violation of constraints. A cell with 1 indicates that the corresponding assignment violates constraints and needs no further consideration. The experimental results showed that the suggested method generated a nurse scheduling faster in time and better in quality compared to the traditional genetic algorithm.

Grid computing is a promising technology for future computing platforms and is expected to provide easier access to remote computational resources that are usually locally limited. Scheduling is one of the core steps to efficiently exploit the capabilities of grid computing (GC) systems. The problem of optimally mapping (defined as matching and scheduling) tasks onto the machines of a grid computing environment has been shown, in general, to be NPcomplete, requiring the development of heuristic techniques. The efficient scheduling of independent tasks in a heterogeneous computing environment is an important problem in domains such as grid computing. Different criteria can be used for evaluating the efficiency of scheduling algorithms, the most important of which are makespan, resource utilization and matching proximity. In this paper we will compare 7 popular heuristics for statically mapping independent tasks onto grid computing systems.

This paper presents a parallel hybrid evolutionary algorithm executed in a grid environment. The algorithm executes local searches using Simulated Annealing within a Genetic Algorithm to solve the Job Shop Scheduling Problem. Experimental results of the algorithm obtained in the “Tarantula MiniGrid” are shown. Tarantula was implemented by linking two clusters from different geographic locations in Mexico (Morelos-Veracruz). The technique used to link the two clusters and conFigureure the Tarantula MiniGrid is described. The effects of latency in communication between the two clusters are discussed. It is shown that the evolutionary algorithm presented is more efficient working in Grid environments because it can carry out major exploration and exploitation of the solution space.

The simulated annealing was perceived as a useful method for many intractable problems. However, it needs additional strategies to cope with time complexity due to an initial state and search space reduction. In this work, we suggested an efficient transition rule and ap-plied it to a nurse scheduling problem. It uses a cost matrix to reduce a set of candidates, which results in performance improvement. The experimental results showed that the sug-gested method generated a feasible solution for the nurse scheduling problem faster in time and better in quality compared to traditional simulated annealing.