Energy-Saving Distributed Flexible Job Shop Scheduling Optimization with Dual Resource Constraints Based on Integrated Q-Learning Multi-Objective Grey Wolf Optimizer

The distributed flexible job shop scheduling problem(DFJSP)has attracted great attention with the growth of the global manufacturing industry.General DFJSP research only considers machine constraints and ignores worker constraints.As one critical factor of production,effective utilization of worker resources can increase productivity.Meanwhile,energy consumption is a growing concern due to the increasingly serious environmental issues.Therefore,the distributed flexible job shop scheduling problem with dual resource constraints(DFJSP-DRC)for minimizing makespan and total energy consumption is studied in this paper.To solve the problem,we present a multi-objective mathematical model for DFJSP-DRC and propose a Q-learning-based multi-objective grey wolf optimizer(Q-MOGWO).In Q-MOGWO,high-quality initial solutions are generated by a hybrid initialization strategy,and an improved active decoding strategy is designed to obtain the scheduling schemes.To further enhance the local search capability and expand the solution space,two wolf predation strategies and three critical factory neighborhood structures based on Q-learning are proposed.These strategies and structures enable Q-MOGWO to explore the solution space more efficiently and thus find better Pareto solutions.The effectiveness of Q-MOGWO in addressing DFJSP-DRC is verified through comparison with four algorithms using 45 instances.The results reveal that Q-MOGWO outperforms comparison algorithms in terms of solution quality.

Resource constraints and firm innovation:When less is more?

This paper explores the relationship between resource constraints and innovation of new firms.Drawing upon the relevant literature,we incorporate resource constraints as the antecedent to the bricolage-innovation link.Compared to prior studies that treated resource constraints as a one-dimensional variable,we operationalize it along two dimensions:knowledge constraints and financial constraints.Our argument posits that knowledge constraints and financial constraints act as catalysts for innovation in new firms,with bricolage serving as a mediating role.To test our hypotheses,we conducted a survey involving 183 entrepreneurs in the United States.The data analysis demonstrates that bricolage fully mediates the relationship between knowledge con‐straints and innovation and partially mediates the relationship between financial constraints and innovation.Theoretical and practical implications are discussed.

Image categorization with resource constraints： introduction, challenges and advances

As one of the most classic fields in computer vi- sion, image categorization has attracted widespread interests. Numerous algorithms have been proposed in the community, and many of them have advanced the state-of-the-art. How- ever, most existing algorithms are designed without consider- ation for the supply of computing resources. Therefore, when dealing with resource constrained tasks, these algorithms will fail to give satisfactory results. In this paper, we provide a comprehensive and in-depth introduction of recent develop- ments of the research in image categorization with resource constraints. While a large portion is based on our own work, we will also give a brief description of other elegant algo- rithms. Furthermore, we make an investigation into the re- cent developments of deep neural networks, with a focus on resource constrained deep nets.

Gini Coefficient-based Task Allocation for Multi-robot Systems With Limited Energy Resources

Nowadays, robots generally have a variety of capabilities, which often form a coalition replacing human to work in dangerous environment, such as rescue, exploration, etc. In these operating conditions, the energy supply of robots usually cannot be guaranteed. If the energy resources of some robots are consumed too fast, the number of the future tasks of the coalition will be affected. This paper will develop a novel task allocation method based on Gini coefficient to make full use of limited energy resources of multi-robot system to maximize the number of tasks. At the same time, considering resources consumption,we incorporate the market-based allocation mechanism into our Gini coefficient-based method and propose a hybrid method,which can flexibly optimize the task completion number and the resource consumption according to the application contexts.Experiments show that the multi-robot system with limited energy resources can accomplish more tasks by the proposed Gini coefficient-based method, and the hybrid method can be dynamically adaptive to changes of the work environment and realize the dual optimization goals.

Improved differential evolution algorithm for resource-constrained project scheduling problem

An improved differential evolution（IDE）algorithm that adopts a novel mutation strategy to speed up the convergence rate is introduced to solve the resource-constrained project scheduling problem（RCPSP）with the objective of minimizing project duration Activities priorities for scheduling are represented by individual vectors and a senal scheme is utilized to transform the individual-represented priorities to a feasible schedule according to the precedence and resource constraints so as to be evaluated.To investigate the performance of the IDE-based approach for the RCPSP,it is compared against the meta-heuristic methods of hybrid genetic algorithm（HGA）,particle swarm optimization（PSO） and several well selected heuristics.The results show that the proposed scheduling method is better than general heuristic rules and is able to obtain the same optimal result as the HGA and PSO approaches but more efficient than the two algorithms.

Minimal feasible sets in variable resource constrained projects

To determine the reasonable resource dependent relations between activities for the purpose of exactly computing the total floats and the free floats of activities, correctly identifying critical activities and critical sequences in a project schedule with variable resource constraints, the concept of the minimal feasible set （MFS） is proposed and the properties of MFS are discussed. The methods to identify optimal MFSs and resource links are then studied. Furthermore, MFS is generalized to the situation that the preconditions of MFS are not satisfied. Contrastive results show that in establishing resource links and resolving floats, MFS is at least not inferior to other methods in all cases and is superior in most situations.

Concurrent processes scheduling with scarce resources in small and medium enterprises

Scarce resources,precedence and non-determined time-lag are three constraints commonly found in small and medium manufacturing enterprises(SMEs),which are deemed to block the application of workflow management system(WfMS). To tackle this problem,a workflow scheduling approach is proposed based on timing workflow net(TWF-net) and genetic algorithm(GA). The workflow is modelled in a form of TWF-net in favour of process simulation and resource conflict checking. After simplifying and reconstructing the set of workflow instance,the conflict resolution problem is transformed into a resource-constrained project scheduling problem(RCPSP),which could be efficiently solved by a heuristic method,such as GA. Finally,problems of various sizes are utilized to test the performance of the proposed algorithm and to compare it with first-come-firstserved(FCFS) strategy. The evaluation demonstrates that the proposed method is an overwhelming and effective approach for scheduling the concurrent processes with precedence and resource constraints.

Mixed Self-adapting GA Optimal Scheduling Algorithm for a Multiple Resource Job-shop

With an aim at the job-shop scheduling problem of multiple resource constraints, this paper presents mixed self-adapting Genetic Algorithm （ GA ） , and establishes a job-shop optimal scheduling model of multiple resource constraints based on the effect of priority scheduling rules in the heuristic algorithm upon the scheduling target. New coding regulations or rules are designed. The sinusoidal function is adopted as the self-adapting factor, thus making cross probability and variable probability automatically change with group adaptability in such a way as to overcome the shortcoming in the heuristic algorithm and common GA, so that the operation efficiency is improved. The results from real example simulation and comparison with other algorithms indicate that the mixed self-adapting GA algorithm can well solve the job-shop optimal scheduling problem under the constraints of various kinds of production resources such as machine-tools and cutting tools.

Resource-constrained Project Scheduling with Overlapping Activities Based on Extended Dependency Structure Matrix

Dependency Structure Matrix （DSM） is a successful and powerful tool for representing and analyzing dependencies between the items, but for external influencing factors it cannot charge effectively. This paper sets the stage for connecting the activities and resources, which not only considers information flow but also resources constrains.We first introduce the DSM to represent the degree of overlapping between the activities in a project. Then we present the Extended DSM combined former DSM and resource factors to calculate the project duration. Finally, the practical significance of the Extended DSM is confirmed by an illustrative example.

Planning projects with scarce resources:Yesterday,today and tomorrow's research challenges

This paper is an invited request to describe the main research challenges in the domain of resourceconstrained project scheduling. The paper is split up in three parts. In today's challenges, research endeavors that have received a significant, but still not enough, attention have been described. In tomorrow's research challenges,some promising research avenues for future research have been given. Finally, in yesterday's challenge, a research topic that started decades ago, is said to have still a huge potential in tomorrow's research agenda. This paper does not intend to give a full literature overview, nor a summary of all possible research paths. Instead, it is inspired from the author's experience in academic research and practical consultancy and it serves as a personal opinion on a nonexhaustive set of promising research avenues, rather than giving a full literature-based advice for future research directions.

Rough set based optimization method of machine tools in cloud manufacturing

Cloud Manufacturing(CMfg),combining with the technologies of Cloud computing and Internet of Things,is an intelligent networked manufacturing model,which can quickly integrate various distributed manufacturing resources for collaboratively completing the complex and customized manufacturing tasks.One of the key technologies supporting this model is the optimal manufacturing resources in the CMfg systems,typically machine tools(MTs).In this paper,the attributes of MTs in cloud environment are analyzed,the constraint relationship between the attributes and the optimization criteria of MTs is established,and an optimization method of MTs based on rough set is proposed.Finally,a case study is discussed to validate the feasibility and effectiveness of the proposed method.