Linyan Sun

A hybrid genetic and variable neighborhood descent algorithm for flexible job shop scheduling problems

This paper addresses the flexible job shop scheduling problem (fJSP) with three objectives: min makespan, min maximal machine workload and min total workload. We developed a hybrid genetic algorithm (GA) for the problem. The GA uses two vectors to represent solutions. Advanced crossover and mutation operators are used to adapt to the special chromosome structure and the characteristics of the problem. In order to strengthen the search ability, individuals of GA are first improved by a variable neighborhood descent (VND), which involves two local search procedures: local search of moving one operation and local search of moving two operations. Moving an operation is to delete the operation, find an assignable time interval for it, and allocate it in the assignable interval. We developed an efficient method to find assignable time intervals for the deleted operations based on the concept of earliest and latest event time. The local optima of moving one operation are further improved by moving two operations simultaneously. An extensive computational study on 181 benchmark problems shows the performance of our approach.

A hybrid of genetic algorithm and bottleneck shifting for multiobjective flexible job shop scheduling problems

Flexible job shop scheduling problem (fJSP) is an extension of the classical job shop scheduling problem, which provides a closer approximation to real scheduling problems. This paper addresses the fJSP problem with three objectives: min makespan, min maximal machine workload and min total workload. We develop a new genetic algorithm hybridized with an innovative local search procedure (bottleneck shifting) for the problem. The genetic algorithm uses two representation methods to depict solution candidates of the fJSP problem. Advanced crossover and mutation operators are proposed to adapt to the special chromosome structures and the characteristics of the problem. The bottleneck shifting works over two kinds of effective neighborhood, which use interchange of operation sequences and assignment of new machines for operations on the critical path. In order to strengthen search ability, the neighborhood structure can be adjusted dynamically in the local search procedure. The performance of the proposed method is tested by numerical experiments on a large number of representative problems.

Scheduling jobs and maintenances in flexible job shop with a hybrid genetic algorithm

Most flexible job shop scheduling models assume that the machines are available all of the time. However, in most realistic situations, machines may be unavailable due to maintenances, pre-schedules and so on. In this paper, we study the flexible job shop scheduling problem with availability constraints. The availability constraints are non-fixed in that the completion time of the maintenance tasks is not fixed and has to be determined during the scheduling procedure. We then propose a hybrid genetic algorithm to solve the flexible job shop scheduling problem with non-fixed availability constraints (fJSP-nfa). The genetic algorithm uses an innovative representation method and applies genetic operations in phenotype space in order to enhance the inheritability. We also define two kinds of neighbourhood for the problem based on the concept of critical path. A local search procedure is then integrated under the framework of the genetic algorithm. Representative flexible job shop scheduling benchmark problems and fJSP-nfa problems are solved in order to test the effectiveness and efficiency of the suggested methodology.

Service-oriented manufacturing: a new product pattern and manufacturing paradigm

Service plays an increasingly important role in modern manufacturing: (a) Services and physical products are integrated into one product service system (PSS) to provide a comprehensive solution for customers; (b) The companies involved in offering PSS focus on specialized sectors, and provide producer services for one another. In this paper, the new product pattern together with the innovative manufacturing paradigm is called service-oriented manufacturing. The competitive advantage of a PSS can be originated from products or services, and the ownership of PSS’s may or may not be transferred from sellers to buyers during transactions. Various PSS’s were categorized into three classes. The characteristics of each type of PSS’s and the shift between them are discussed. Many companies, which provide producer services and manufacturing services to one another, form a service-based manufacturing network. The reasons why producer services act as intermediate goods among different companies and motivations for companies to outsource their business processes are analyzed economically. Many companies in different segments of the production-chain may have discrepant profitability. Technology strength and industry insight competences are adopted to explain the discrepant values added from various segments along the production chain. Service-oriented manufacturing is summarized from the perspectives of business model, industry insight and technology strength (BIT).

An efficient approach for type II robotic assembly line balancing problems

In the past decades, robots have been extensively applied in assembly systems as called robotic assembly lines. When changes in the production process of a product take place, the line needs to be reconfigured in order to improve its productivity. This study presents a type II robotic assembly line balancing (rALB-II) problem, in which the assembly tasks have to be assigned to workstations, and each workstation needs to select one of the available robots to process the assigned tasks with the objective of minimum cycle time. An innovative genetic algorithm (GA) hybridized with local search is proposed for the problem. The genetic algorithm uses a partial representation technique, where only part of the decision information about a candidate solution is expressed in the chromosome and the rest is computed via a heuristic method. Based on different neighborhood structures, five local search procedures are developed to enhance the search ability of GA. The coordination between these procedures is well considered in order to escape from local optima and to reduce computation time. The performance of the hybrid genetic algorithm (hGA) is tested on 32 rALB-II problems and the obtained results are compared with those by other methods.

Single-machine group scheduling with resource allocation and learning effect

This paper addresses single-machine scheduling problems under the consideration of learning effect and resource allocation in a group technology environment. In the proposed model of this paper the actual processing times of jobs depend on the job position, the group position, and the amount of resource allocated to them concurrently. Learning effect and two resource allocation functions are examined for minimizing the weighted sum of makespan and total resource cost, and the weighted sum of total completion time and total resource cost. We show that the problems for minimizing the weighted sum of makespan and total resource cost remain polynomially solvable. We also prove that the problems for minimizing the weighted sum of total completion time and total resource cost have polynomial solutions under certain conditions.

A multi-objective genetic algorithm for mixed-model assembly line rebalancing

When demand structure or production technology changes, a mixed-model assembly line (MAL) may have to be reconfigured to improve its efficiency in the new production environment. In this paper, we address the rebalancing problem for a MAL with seasonal demands. The rebalancing problem concerns how to reassign assembly tasks and operators to candidate stations under the constraint of a given cycle time. The objectives are to minimize the number of stations, workload variation at each station for different models, and rebalancing cost. A multi-objective genetic algorithm (moGA) is proposed to solve this problem. The genetic algorithm (GA) uses a partial representation technique, where only a part of the decision information about a candidate solution is expressed in the chromosome and the rest is computed optimally. A non-dominated ranking method is used to evaluate the fitness of each chromosome. A local search procedure is developed to enhance the search ability of moGA. The performance of moGA is tested on 23 reprehensive problems and the obtained results are compared with those by other authors.

Single-machine scheduling time-dependent jobs with resource-dependent ready times

In the paper resource constrained two single-machine scheduling problems with deteriorating jobs are considered. We model job deterioration as a function that is proportional to a linear function of time. It is assumed that the release time of a job is a positive strictly decreasing continuous function of the amount of consumed resource. We present polynomial solutions for the total resource consumption minimization problem under the constraint that the makespan does not exceed a given limit, and the makespan minimization problem under the constraint that the total resource consumption does not exceed a given limit, respectively.

Anticipated rationing policy for two demand classes under service level constraints

This paper considers a periodic-review, infinite-horizon, backorder inventory model with two demand classes, where a base-stock policy controls replenishment. We propose an easy-to-use rationing policy, which reserves inventory for future high priority demands by taking the coming delivery of the next period into consideration, hence called anticipated rationing policy. By applying a multidimensional Markov chain approach, we are able to evaluate system performance exactly. We also derive structural results and find optimal solutions for both @a-service level and @c-service level constraint problems. A numerical comparison study demonstrates the effectiveness of anticipated rationing policy, where the well-known constant level rationing policy serves as the benchmark.

Coordinating preventive lateral transshipment between two locations

We analyze preventive transshipment between two locations in anticipation of the mismatch between demands and inventories, and the effects of the preventive transshipment on ordering quantities. The time horizon for preventive transshipment includes two stages: the ordering stage and the shipping stage. At the ordering stage, the two locations order products from their supplier. During the replenishment lead-time, some demand signals (e.g., the realized demand for a complementary product) may be observed. Therefore, the locations may update their demand distributions and preventively transship to each other at the shipping stage. When the two locations make their ordering and transshipping decisions individually to maximize their own profits, there are incentive problems that prevent coordination. These problems arise even between the locations that pay each other for transshipped units. We examine two commonly used linear transfer price contracts: the ex ante transfer price contract and the ex post transfer price contract. However, neither of these contracts coordinates the transshipment quantities between the two locations. We then present a bidirectional revenue sharing contract that can coordinate the transshipment quantities. We find the conditions under which this proposed contract coordinates the ordering quantities. Finally, we investigate how the transportation cost and the amount of information updating affect the ordering quantities with the coordinating bidirectional revenue sharing contract.