A Pareto approach to multi-objective flexible job-shop scheduling problem using particle swarm optimization and local search

The job-shop scheduling problem is one of the most arduous combinatorial optimization problems. Flexible job-shop problem is an extension of the job-shop problem that allows an operation to be processed by any machine from a given set along different routes. This paper present a new approach based on a hybridization of the particle swarm and local search algorithm to solve the multi-objective flexible job-shop scheduling problem. The particle swarm optimization is a highly efficient and a new evolutionary computation technique inspired by birds’ flight and communication behaviors. The multi-objective particle swarm algorithm is applied to the flexible job-shop scheduling problem based on priority. Also the presented approach will be evaluated for their efficiency against the results reported for similar algorithms (weighted summation of objectives and Pareto approaches). The results indicate that the proposed algorithm satisfactorily captures the multi-objective flexible job-shop problem and competes well with similar approaches.     

Scheduling a single batch processing machine with arbitrary job sizes and incompatible job families

Motivated by a bottleneck operation in a multi-layer ceramic capacitor production line, we study a scheduling problem of batch processing machine in which a number of jobs are processed simultaneously as a batch. The performance measures considered include makespan, total completion time, and total weighted completion time. We first present a new simple integer programming formulation for the problem, and using this formulation, one can easily find optimal solutions for small problems. However, since the problem is NP-hard and the size of a real problem is very large, we propose a number of heuristic algorithms and design a hybrid genetic algorithm to solve practical big-size problems in a reasonable computational time. To verify performance of the algorithms, we compare them with lower bounds for the problems. From the results of these computational experiments the heuristic algorithms including the genetic algorithm show different performances for the three problems.     

跳汰机自动控制系统的设计与实践

跳汰机选煤因工艺简单、投资少和易于操作等特点,在选煤工业中占据重要地位。为解决目前老跳汰机自动化水平低、测控精度差等问题,提出了改造跳汰机自动控制系统的整套方案,解决了老跳汰机存在的一系列问题,提高了选煤质量。系统实际运行表明,该系统具有很多优点且具有一定的实用价值和推广价值。     

Hybrid flow shop scheduling with parallel batching

In this research the problem of parallel batch scheduling in a hybrid flow shop environment with minimizing C_max is studied. In parallel batching it is assumed that machines in some stages are able to process a number of operations simultaneously. Since the problem is NP-hard, three heuristic algorithms are developed to give near optimal solutions. Since this problem has not been studied previously, therefore, a lower bound is developed for evaluating the performance of the proposed heuristics. Several test problems have been solved using these heuristics and results compared. To further enhance the solution quality, a three dimensional genetic algorithm (3DGA) is also developed. Several test problems have been solved using 3DGA and the results indicate its superiority to the other heuristics.     

Two-machine flow shop scheduling of polyurethane foam production

This paper studies a two-machine flow shop scheduling problem with a supporting precedence relation. The model originates from a real production context of a chemical factory that produces foam-rubber products. We extend the traditional two-machine flow shop by dividing the operations into two categories: supporting tasks and regular jobs. In the model, several different compositions of foam rubber can be mixed at the foam blooming stage, and products are processed at the manufacturing stage. Each job (product) on the second machine cannot start until its supporting tasks (parts) on the first machine are all finished and the second machine is not occupied. The objective is to find a schedule that minimizes the total job completion time. The studied problem is strongly NP-hard. In this paper, we propose a branch-and-bound algorithm incorporating a lower bound and two dominance rules. We also design a simple heuristic and an iterated local search (ILS) algorithm to derive approximate solutions. The performances of the proposed algorithms are examined through computational experiments.     

Scheduling a capacitated single machine with time deteriorating job values

We address the scheduling problem with the following characteristics: (i) there is a single machine available, (ii) the machine has limited capacity, and (iii) job value deteriorates with time. The problem is motivated from several real world situations, such as, downloading process of web pages, and scheduling of multiplexes. Since the problem is NP-hard, we propose new heuristics based on a multiplicative piece-wise metric as an approximation of the slope of job value deterioration. Computational results show that the proposed heuristics perform better than other existing heuristics for similar types of problems.     

A tabu search approach for cell scheduling problem with makespan criterion

Scheduling problem in a cellular manufacturing system is treated as the group scheduling problem, assuming that intercellular moves can be eliminated by duplicating machines. However, in a typical CMS, duplicating bottleneck machines may be costly and infeasible. This fact limits the applicability of group scheduling. Scheduling problem in the presence of bottleneck machines is termed as cell scheduling. A mixed-integer linear programming model is proposed for the attempted cell scheduling problem and a nested application of tabu search approach is investigated in this paper to solve the problem heuristically. The effectiveness of the proposed nested tabu search (NTS) algorithm is evaluated on 16 problems selected from the literature. Comparison of the results of NTS with SVS-algorithm reveals the effectiveness and efficiency of the proposed algorithm.     

Two-stage flowshop earliness and tardiness machine scheduling involving a common due window

This paper studies a non-preemptive two-stage flowshop scheduling problem to minimize the earliness and tardiness under the environment of a common due window. The window size and the window location are considered to be given parameters. The just-in-time problem exists naturally and has many practical applications. The problem is shown to be NP-complete in the strong sense. We develop a branch and bound algorithm and a heuristic to solve the problem. We conduct the computational experiments to test the performances of the algorithms. A strong lower bound is derived for the branch and bound algorithm that can efficiently solve 15 jobs problem for about 5 minutes. The heuristic is shown to be efficient and effective, which can solve the problem of 150 jobs for about 20 seconds and provide near-optimal solution. We justify that the heuristic is an excellent solution approach for large problem instances. We also show that four special cases are either polynomial solvable or NP-complete in the ordinary sense.     

Heuristics for operational fixed job scheduling problems with working and spread time constraints

Operational fixed job scheduling problems select a set of jobs having fixed ready and processing times and schedule the selected jobs on parallel machines so as to maximize the total weight. In this study, we consider working time and spread time constrained versions of the operational fixed job scheduling problems. The working time constraints limit the total processing load on each machine. The spread time constraints limit the time between the start of the first job and the finish of the last job on each machine. For the working time constrained problem, we present a filtered beam search algorithm that evaluates the promising nodes of the branch and bound tree. For the spread time constrained problem we propose a two phase algorithm that defines the promising sets for the first jobs and finds a solution for each promising set. The results of our computational tests reveal that our heuristic algorithms perform very well in terms of both solution quality and time.     

A new approach to job shop scheduling problems with due date constraints considering operation subcontracts

We consider a kind of job shop scheduling problems with due-date constraints, where temporal relaxation of machine capacity constraint is possible through subcontracts. In practice, this kind of problem is frequently found in manufacturing industries where outsourcing of manufacturing operation is possible through subcontract. We present a heuristic algorithm that addresses the problem by solving a series of smaller subproblems to optimality. For the sake of efficiency, the algorithm repeatedly executes in two steps—(1) improving the sequence of operations and (2) picking out the operations to be subcontracted—on bottleneck machines. Experiments are conducted for example problems, and the result of the experiment confirms the viability of the suggested algorithm.     

Two-machine flow shop scheduling with deteriorating jobs and chain precedence constraints

In this paper we investigate two-machine flow shop scheduling problems with deteriorating jobs and chain precedence constraints. We consider two types of precedence constraints. Under the first type constraints, a successor cannot start on any machine before its predecessor has been completed on the same machine. Under the second one, a successor cannot start on any machine before its predecessor has been completed on all machines. We show that the problem with the first type precedence constraints is polynomially solvable, and the problem with the second type precedence constraints is NP-hard in the strong sense.     

A hybrid artificial bee colony algorithm for the job shop scheduling problem

The job shop scheduling problem (JSSP) has attracted much attention in the field of both information sciences and operations research. In terms of the objective function, most existing research has been focused on the makespan criterion (i.e., minimizing the overall completion time). However, for contemporary manufacturing firms, the due date related performance is usually more important because it is crucial for maintaining a high service reputation. Therefore, in this study we aim at minimizing the total weighted tardiness in JSSP. Considering the high complexity, a novel artificial bee colony (ABC) algorithm is proposed for solving the problem. A neighborhood property of the problem is discovered, and then a tree search algorithm is devised to enhance the exploitation capability of ABC. According to extensive computational tests, the proposed approach is efficient in solving the job shop scheduling problem with total weighted tardiness criterion.     

膜生物反应器处理废水的研究及应用

膜生物反应器将生物处理与膜分离技术相结合,是一种高效的废水处理新技术。在城市污水处理、中水回用、工业废水处理等方面的研究及应用实践表明,膜生物反应器具有常规废水处理工艺无法比拟的优势。根据国内外的研究及应用现状介绍了膜生物反应器的类型、特点及在废水处理中的应用,并对其在废水处理与回用中的应用前景进行了展望。     

Revenue management for make-to-order manufacturing systems with a real-life application

Abstract

Due to successful applications of revenue management in the airline industry, in recent years, there has been a growing interest to adopt revenue management in make-to-order (MTO) manufacturing systems. Several interrelated decision problems such as order acceptance/rejection, short-term capacity planning, due date assignment, and order scheduling need to be studied simultaneously in order to manage revenues effectively in MTO manufacturing systems. Both the producer’s and customer’s requirements need to be taken into account through some negotiation mechanisms that are sensitive to the service-level reputation of the manufacturing companies. In this article, we propose a new dynamic bid price–based revenue management model that considers all of the aforementioned decision problems simultaneously. A simulation optimization approach is utilized in order to determine the best possible values of control parameters for bid price, due date assignment, and price increment/reduction mechanisms. The performance of the proposed integrated revenue management model is tested on both a hypothetical example and a real problem of a bridal gown company. The computational results show that the proposed model provides significant improvements in total revenue compared to other static and dynamic bid price policies.     

A local search algorithm for the optimization of the stochastic economic lot scheduling problem

This paper describes a model for the stochastic economic lot scheduling problem (SELSP) and a Local Search heuristic to find close to optimal solutions to this model. The SELSP considers multiple products, which have to be scheduled on a single facility with limited capacity and significant setup times and costs. The demand is modeled as a stationary compound renewal process. The objective is to find a schedule that minimizes the long-run average costs for setups and inventories while satisfying a given fill rate. We use a cyclic scheduling approach in which the individual cycle time of each product is a multiple of some basic period (fundamental cycle).For the deterministic version of the SELSP, efficient heuristics have been developed which guarantee the feasibility of the solution by adding an additional constraint to the problem. In our case this is not sufficient, because for the calculation of the average inventory levels and fill rates we need to develop a schedule with detailed timing of the lots. We present an efficient heuristic for this scheduling problem, which can also be used to check the feasibility of the solution. Thereby, the most time-consuming step (the calculation of average inventory levels and fill rates) is only performed for a limited set of candidates.The algorithm was tested on deterministic benchmark problems from literature and on a large set of stochastic instances. We report on the performance of the heuristic in both cases and try to identify the main factors influencing the objective.     

The management of technological innovation in the machine tool industry: a cross-national regional survey of Britain and Switzerland

Abstract
The research was undertaken during 1983 to find out why a country such as Switzerland has been able to manage innovation in a mature industry, such as machine tools, much more successfully than Britain. The methodology used was by structured interviews of Managing Directors in a sample of firms in the two important machine tool regions of Berne and Yorkshire/Humberside. The results cover the main types of innovation in products and processes, the reasons for innovation and the problems which were encountered by the companies visited. We found that the Swiss firms were strong on product innovation. The most important innovation was the introduction of computer numerical control (CNC), and the Swiss firms had on average introduced this earlier than the British firms and it accounted for a larger proportion of their turnover. The main problem for firms in both countries was in adjusting to upstream developments in electronic controls. The Swiss had better downstream market links with their customers who were mostly in foreign countries. Some British firms were handicapped by being part of company groups and used public sector grants for their innovation, whereas the largely independent Swiss firms carried out more self-financing.     

Robust scheduling for multi-objective flexible job-shop problems with random machine breakdowns

This study addresses robust scheduling for a flexible job-shop scheduling problem with random machine breakdowns. Two objectives – makespan and robustness – are simultaneously considered. Robustness is indicated by the expected value of the relative difference between the deterministic and actual makespan. Utilizing the available information about machine breakdowns, two surrogate measures for robustness are developed. Specifically, the first suggested surrogate measure considers the probability of machine breakdowns, while the second surrogate measure considers the location of float times and machine breakdowns. To address this problem, a multi-objective evolutionary algorithm is presented in this paper. The experimental results indicate that, compared with several other existing surrogate measures, the first suggested surrogate measure performs better for small cases, while the second surrogate measure performs better for both small and relatively large cases.     

A note on constructive heuristics for the flowshop problem with blocking

This paper analyzes the minimization of the makespan criterion for the flowshop problem with blocking. In this environment, there are no buffers between successive machines, and therefore intermediate queues of jobs waiting in the system for their next operations are not allowed. As the problem is NP-hard, a constructive heuristic that explores specific characteristics of the problem is developed. The small computational effort of such strategy, which is valuable in practical applications, is one of the reasons that motivated this study. The performance of a combination of the proposed method with existing ones is examined through a comparative study. The new methods outperform the NEH algorithm, currently the best constructive heuristic for this problem, in problems with up to 500 jobs and 20 machines.     

Non-permutation flowshop scheduling in a supply chain with sequence-dependent setup times

In this paper, we consider a flowshop scheduling problem with sequence-dependent setup times and a bicriteria objective to minimize the work-in-process inventory for the producer and to maximize the customers' service level. The use of a bicriteria objective is motivated by the fact that successful companies in today's environment not only try to minimize their own cost but also try to fulfill their customers' need. Two main approaches, permutation and non-permutation schedules, are considered in finding the optimal schedule for a flowshop. In permutation schedules the sequence of jobs remains the same on all machines whereas in non-permutation schedule, jobs can have different sequence on different machines. A linear mathematical model for solving the non-permutation flowshop is developed to comply with all of the operational constraints commonly encountered in the industry, including dynamic machine availabilities, dynamic job releases, and the possibility of jobs skipping one or more machines, should their operational requirements deem that it was necessary. As the model is shown to be NP-hard, a metasearch heuristic, employing a newly developed concept known as the Tabu search with embedded progressive perturbation (TSEPP) is developed to solve, in particular, industry-size problems efficiently. The effectiveness and efficiency of the search algorithm are assessed by comparing the search algorithmic solutions with that of the optimal solutions obtained from CPLEX in solvable small problem instances.     

Simulation theory can provide practical solutions to complex industrial marketing problems

To maintain adequate levels of production and profit despite prevailing economic recession and inflation, many firms have explored various sectors of potential cost savings within the logistics framework. Models, algorithms, and mathematical-solution techniques have been devised and implemented for warehouse layout and/or location, inventory control, and other problem areas. For instance, for many distribution managers the acceptance of the public warehouse and the centralized distribution center as solutions to profit-squeeze problems has brought to the forefront the intricate problems and high costs associated with advanced shipment scheduling and delivery techniques.