A decision support system for personnel scheduling in a manufacturing environment

A Decision Support System (DSS) is developed to support managers in the task of scheduling labor in the area of manufacturing. The DSS is designed to generate labor requirements by worker category and work center based on master production schedules. It is a PC-based, menu-driven program that generates a capacity plan based on data supplied by the user of the system.     

A hybrid intelligent model for order allocation planning in make-to-order manufacturing

This paper investigated a multi-objective order allocation planning problem in make-to-order manufacturing with the consideration of various real-world production features. A novel hybrid intelligent optimization model, integrating a multi-objective memetic optimization (MOMO) process, a Monte Carlo simulation technique and a heuristic pruning technique, is developed to tackle this problem. The MOMO process, combining a NSGA-II optimization process with a tabu search, is proposed to provide Pareto optimal solutions. Extensive experiments based on industrial data are conducted to validate the proposed model. Results show that (1) the proposed model can effectively solve the investigated problem by providing effective production decision-making solutions; (2) the MOMO process has better capability of seeking global optimum than an NSGA-II-based optimization process and an industrial method.     

Interactive job sequencing system for small make-to-order manufacturers under smart manufacturing environment

Peer-to-Peer Networking and Applications - Production scheduling is an important research topic widely studied during past few decades. However, many manufacturers still fail to successfully deploy...     

A computer simulation model for job shop scheduling problems minimizing makespan

One of the basic and significant problems, that a shop or a factory manager is encountered, is a suitable scheduling and sequencing of jobs on machines. One type of scheduling problem is job shop scheduling. There are different machines in a shop of which a job may require some or all these machines in some specific sequence. For solving this problem, the objective may be to minimize the makespan. After optimizing the makespan, the jobs sequencing must be carried out for each machine. The above problem can be solved by a number of different methods such as branch and bound, cutting plane, heuristic methods, etc. In recent years, researches have used genetic algorithms, simulated annealing, and machine learning methods for solving such problems. In this paper, a simulation model is presented to work out job shop scheduling problems with the objective of minimizing makespan. The model has been coded by Visual SLAM which is a special simulation language. The structure of this language is based on the network modeling. After modeling the scheduling problem, the model is verified and validated. Then the computational results are presented and compared with other results reported in the literature. Finally, the model output is analyzed.     

A two-stage hybrid manufacturing model with controllable make-to-order production rates

As an effective strategy to facilitate delivering customized products within short lead time, hybrid manufacturing via a two-stage process has received attention from academia and industry. In this paper, we study a two-stage hybrid manufacturing system in which semifinished products are manufactured in a make-to-stock fashion in the first stage and end-products are produced from semifinished goods in a make-to-order (MTO) mode in the second stage. The rate of MTO production can be controlled within given limits, depending on the status of the system. The primary goal of this paper is to study a policy for coordinating order admission, MTO production rate, and inventory replenishment controls. Formulating the problem as a Markov decision process model, we characterize the structure of optimal control policies to maximize the long-run average profit. Using a numerical experiment, we study how the flexibility in MTO production rate affects the optimal policy and the optimal profit. We also examine the effect of the number of alternative MTO production rates on the optimal profit. We propose three heuristic policies implementable for general cases. The first heuristic describes two linear switching functions for admission and production controls and a selection rule for MTO production rate control. The second heuristic specifies fixed thresholds for the control decisions using the local information. The third heuristic presents linear switching functions that approximate the optimal threshold curves. Unlike second and third heuristics, the first heuristic does not require a grid search to determine the control parameters. We implement numerical studies to examine the marginal impact of system parameters and the effect of the number of alternative MTO production rates on the performance of the heuristics. Compared to the optimal policy, the average percentage performance of the first and third heuristics is less than 1% for both numerical studies. On the other hand, the average percentage performance of the second heuristic is larger than 3%, and it exceeds 10% for a set of particular problem examples.     

A hybrid differential evolution algorithm for job shop scheduling problems with expected total tardiness criterion

In real-world manufacturing systems, the processing of jobs is frequently affected by various unpredictable events. However, compared with the extensive research for the deterministic model, study on the random factors in job shop scheduling has not received sufficient attention. In this paper, we propose a hybrid differential evolution (DE) algorithm for the job shop scheduling problem with random processing times under the objective of minimizing the expected total tardiness (a measure for service quality). First, we propose a performance estimate for roughly comparing the quality of candidate solutions. Then, a parameter perturbation algorithm is applied as a local search module for accelerating the convergence of DE. Finally, the K-armed bandit model is utilized for reducing the computational burden in the exact evaluation of solutions based on simulation. The computational results on different-scale test problems validate the effectiveness and efficiency of the proposed approach.     

Real-time scheduling for hybrid flowshop in ubiquitous manufacturing environment

This paper discusses the implementation of RFID technologies, which enable the shop floor visibility and reduce uncertainties in the real-time scheduling for hybrid flowshop (HFS) production. In the real-time HFS environment, the arriving of new jobs is dynamic, while the processes in work stages are not continuous. The decision makers in shop floor level and stage level have different objectives. Therefore, classical off-line HFS scheduling approaches cannot be used under these situations. In this research, two major measures are taken to deal with these specific real-time features. Firstly, a ubiquitous manufacturing (UM) environment is created by deploying advanced wireless devices into value-adding points for the collection and synchronization of real-time shop floor data. Secondly, a multi-period hierarchical scheduling (MPHS) mechanism is developed to divide the planning time horizon into multiple shorter periods. The shop floor manager and stage managers can hierarchically make decisions for their own objectives. Finally, the proposed MPHS mechanism is illustrated by a numerical case study.     

A heuristic for job shop scheduling to minimize total weighted tardiness

This paper considers the job shop scheduling problem to minimize the total weighted tardiness with job-specific due dates and delay penalties, and a heuristic algorithm based on the tree search procedure is developed for solving the problem. A certain job shop scheduling to minimize the maximum tardiness subject to fixed sub-schedules is solved at each node of the search tree, and the successor nodes are generated, where the sub-schedules of the operations are fixed. Thus, a schedule is obtained at each node, and the sub-optimum solution is determined among the obtained schedules. Computational results on some 10 jobs and 10 machines problems and 15 jobs and 15 machines problems show that the proposed algorithm can find the sub-optimum solutions with a little computation time.     

Trade-off solutions in a single-machine scheduling problem for minimizing total earliness and maximum tardiness

This paper considers a single-machine scheduling problem involving minimization of the total earliness and the maximum tardiness. Four dominant properties for the precedence relationship between jobs in a search for an optimal solution are proposed. The lower bounds of the total earliness and the maximum tardiness of a subproblem are derived. The dominance properties and the lower bounds are implemented in the branchand-bound algorithm to facilitate the search for an optimal schedule. A heuristic algorithm is then developed to overcome the inefficiency of the branch-and-bound algorithm. Computational performance of the two algorithms is also investigated.     

作业车间环境下的采购-生产-配送联合调度模型

针对生产-配送联合调度（IPDS）模型较少考虑复杂生产环境以及采购环节的问题,建立了在作业车间环境下,以最小化订单完成时间为目标的采购-生产-配送联合调度（IPPDS）模型,并采用改进的动态人工蜂群（DABC）算法进行求解。根据IPPDS问题的特征,首先,采用二维实数矩阵的编码方式,实现任务（加工与运输）与资源（设备与车辆）的匹配关系;其次,采用基于工艺过程的解码方式,并在解码过程中针对不同任务设计了满足约束条件的方法,来保证解码方案的可行性;最后,在算法过程中设计了引领蜂与跟随蜂的动态协调机制和局部启发式信息。通过实验给出DABC适当的参数区间,对比实验结果表明,IPPDS策略相较于分段调度和IPDS策略,调度时间分别缩短了35.59%和30.95%;DABC相较于人工蜂群（ABC）算法求解效果平均提升了2.54%,相对于改进的遗传算法（AGA）求解效果平均提升了6.99%。因此,IPPDS策略能更快速地满足客户需求,而DABC算法既减少需设置的参数,又具有良好的探索和开发能力。     

Information fusion for simulation based decision support in manufacturing

Robust and informed decisions are important for the efficient and effective operation of installed production facilities. The paper discusses Information fusion (IF) including a generic model for IF, and situations for decision-making. The paper also discusses current and future use of manufacturing resource simulation for design/configuration, operational planning and scheduling, and service and maintenance of manufacturing systems. Many of these applications use IF in some way, as is explained in more detail for simulation based service and maintenance. An extension of the generic model for IF is presented which incorporates modeling and simulation, and active databases as used in a simulation based service and maintenance system at the authors’ laboratory.     

A genetic local search algorithm for minimizing total weighted tardiness in the job-shop scheduling problem

This paper considers the job-shop problem with release dates and due dates, with the objective of minimizing the total weighted tardiness. A genetic algorithm is combined with an iterated local search that uses a longest path approach on a disjunctive graph model. A design of experiments approach is employed to calibrate the parameters and operators of the algorithm. Previous studies on genetic algorithms for the job-shop problem point out that these algorithms are highly depended on the way the chromosomes are decoded. In this paper, we show that the efficiency of genetic algorithms does no longer depend on the schedule builder when an iterated local search is used. Computational experiments carried out on instances of the literature show the efficiency of the proposed algorithm.     

Multi-phase integrated scheduling of hybrid tasks in cloud manufacturing environment

Cloud manufacturing paradigm aims at gathering distributed manufacturing resources and enterprises to serve for more customized production. Production order which involving several tasks can be taken by distributed suppliers collaboratively at lower cost. The cloud manufacturing platform is responsible for not only arranging reasonable priorities, suitable suppliers, and production processes to multiple orders, but also scheduling hybrid tasks from different orders to manufacturing resources. To maximize the production efficiency and balance the trade-off among different production orders, this paper studies multi-phase integrated scheduling of hybrid tasks in cloud manufacturing environment, which containing order priority assignment, supplier and production process selection, and production line scheduling. Five key objectives are taken into account to analyze the interconnections among different resources and production processes. Six representative multi-objective evolutionary algorithms are adopted to solve the integrated scheduling problem. Experimental results on six production cases show that integrated scheduling is more effective than the traditional step-by-step decision, leading to less production cost and time. In addition, a comparison among the six algorithms is carried out to determine the one best suited for the integrated scheduling problem in different circumstances.     

A manufacturing decision support system for flamecutting

The design of a Manufacturing Decision Support System (MDSS) for the control of a flamecutting operation is discussed. The MDSS incorporates the overall economics of a continuing inventory. It also takes into account the use of left-over offcuts or partial plates, and the possibility of producing as a flow shop instead of on a job shop cut-to-order basis. Some research results are available that aid the construction of such a DSS; these are mostly in the areas of two-dimensional parts' layout and torch path sequencing. The proposed DSS constructs parts' nests, generates cutting sequences, directs the use of trim margins, and updates and outputs the economics of the whole cutting process. Its major strength is its potential use at the shop floor level, using relatively inexpensive computing power to control cutting torches that are usually driven by far more expensive systems that take much longer to determine layouts and cutting sequences.     

Inventory based two-objective job shop scheduling model and its hybrid genetic algorithm

Job shop scheduling problem is a typical NP-hard problem. An inventory based two-objective job shop scheduling model was proposed in this paper, in which both the make-span (the total completion time) and the inventory capacity were as objectives and were optimized simultaneously. To solve the proposed model more effectively, some tailor made genetic operators were designed by making full use of the characteristics of the problem. Concretely, a new crossover operator based on the critical path was specifically designed. Furthermore, a local search operator was designed, which can improve the local search ability of GA greatly. Based on all these, a hybrid genetic algorithm was proposed. The computer simulations were made on a set of benchmark problems and the results demonstrated the effectiveness of the proposed algorithm.     

Integrated decision support and expert systems in a computer integrated manufacturing environment

In a computer integrated manufacturing (CIM) environment, well planning, control and operational process require both expert knowledge of the area, and powerful decision support capabilities. This paper discusses the features of decision support systems and expert systems, and their integration to support the major functions from marketing and strategic level considerations to manufacturing operational planning and process. From the hierarchical structure of information flow in a company, this paper attempts to find the best way of combining decision support systems with expert systems in enhancing the planning, control and operational functions in a CIM environment.     

Development of a simulation-based decision support system for controlling stochastic flexible job shop manufacturing systems

This paper describes a simulation-based decision support system (DSS) to production control of a stochastic flexible job shop (SFJS) manufacturing system. The controller design approach is built around the theory of supervisory control based on discrete-event simulation with an event–condition–action (ECA) real-time rule-based system. The proposed controller constitutes the framework of an adaptive controller supporting the co-ordination and co-operation relations by integrating a real-time simulator and a rule-based DSS. For implementing SFJS controller, the proposed DSS receives online results from simulator and identifies opportunities for incremental improvement of performance criteria within real-time simulation data exchange (SDX). A bilateral method for multi-performance criteria optimization combines a gradient based method and the DSS to control dynamic state variables of SFJS concurrently. The model is validated by some benchmark test problems.     

A hybrid fuzzy AHP-PROMETHEE decision support system for machine tool selection in flexible manufacturing cell

The selection process of a suitable machine tool among the increased number of alternatives has been an important issue for manufacturing companies for years. This is because the improper selection of a machine tool may cause many problems that will affect the overall performance. In this paper, a decision support system (DSS) is presented to select the best alternative machine using a hybrid approach of fuzzy analytic hierarchy process (fuzzy AHP) and preference ranking organization method for enrichment evaluation (PROMETHEE). A MATLAB- based fuzzy AHP is used to determine the weights of the criteria and it is called program for Priority Weights of the Evaluation Criteria (PWEC), and the PROMETHEE method is applied for the final ranking. The proposed model is structured to select the most suitable computer numerical controlled (CNC) turning centre machine for a flexible manufacturing cell (FMC) among the alternatives which are assigned from a database (DB) created for this purpose. A numerical example is presented to show the applicability of the model. It is concluded that the proposed model has the capability of dealing with a wide range of desired criteria and to select any type of machine tool required for building an FMC.     

A scheduling decision aid in glass fiber manufacturing

A decision aid for scheduling production in glass fiber manufacturing industry is described. The methodology combines a linear programming (LP) optimization model with a heuristic model. The LP model determines production goals; the heuristic model then uses the LP output to incorporate system-specific constraints in developing processing sequences.