Backtracking techniques for the job shop scheduling constraint satisfaction problem

Job shop scheduling is a highly nondeterministic polynomial combinatorial issue. In this regard, optimization is essential for reducing the time consumed to perform a task. This research paper proposes an evolved social spider optimization method to deal with the job shop scheduling problem. The evolved social spider optimization method was developed by combining both the differential evolutionary and the social spider optimizations. The key proposal is to minimize the makespan time and solve Job shop scheduling problems to improve productivity. The differential evolutionary algorithm is integrated into the spider position update to boost the exploration capabilities of the social spider optimization algorithm. The time taken for ‘‘i’ jobs and ‘‘j’ machines to perform their tasks is studied. Consequently, 23 benchmark problems were prosperously studied utilizing the proposed techniques, and the computational results were compared with previous Meta heuristics methods. An all-inclusive comparison process was carried out to rate the efficiency of the existing optimization techniques in solving job shop scheduling problems. The proposed method of evolved social spider optimization has emerged as the most promising methodology in solving the job shop scheduling problem by consuming minimum makespan time.

Variable and value ordering heuristics for the job shop scheduling constraint satisfaction problem

Aims: In this paper, we propose an enhanced artificial bee colony (ABC) algorithm for the job shop scheduling problem. Background: Meta-heuristic approaches are widely applied to solve job shop scheduling problem and find the near optimal solutions in polynomial time. Objective: Improving performance of ABC algorithm to solve job shop scheduling problem. Methods: Using local search heuristics in ABC algorithm. Results: The experimental results showed that the proposed algorithm improves the efficiency. Conclusion: Proposed algorithm improves efficiency.

Solving job shop scheduling problems (JSSPs) with a fixed strategy, such as a priority dispatching rule, may yield satisfactory results for several problem instances but, nevertheless, insufficient results for others. From this single-strategy perspective finding a near optimal solution to a specific JSSP varies in difficulty even if the machine setup remains the same. A recent intensively researched and promising method to deal with difficulty variability is Deep Reinforcement Learning (DRL), which dynamically adjusts an agent's planning strategy in response to difficult instances not only during training, but also when applied to new situations. In this paper, we further improve DLR as an underlying method by actively incorporating the variability of difficulty within the same problem size into the design of the learning process. We base our approach on a state-of-the-art methodology that solves JSSP by means of DRL and graph neural network embeddings. Our work supplements the training routine of the agent by a curriculum learning strategy that ranks the problem instances shown during training by a new metric of problem instance difficulty. Our results show that certain curricula lead to significantly better performances of the DRL solutions. Agents trained on these curricula beat the top performance of those trained on randomly distributed training data, reaching 3.2% shorter average makespans.

Solving job shop scheduling problems via deep reinforcement learning

The Job shop scheduling problem is known as a combinatorial optimization problem that aims to find best sequence of operations with optimal execution time called makespan. Various algorithms are used to resolve it. This research paper aims to provide a new adaptation to solve the job shop scheduling problem. Discrete cat swarm optimization algorithm (CSO) consists of two modes, which are: the seeking mode when the cat is resting and the tracing mode when the cat is hunting. These two modes are combined by a mixture ratio. The result applied to some benchmark instances and proves thus the performance of this adaptation.

Job Shop Scheduling Problem (JSSP) is one of the important and tough problem in real world, which tries to schedule N jobs to be performed on M machines. In this paper, it is aimed to imply this problem in computer science and also bringing up many related solution models. Although, most of these models try to find an optimal approach, Ant Colony Optimization (ACO) constitutes the optimal solution by using some of the complex ACO scenarios effectively. In this study, it is focused on one of the scenario of the JSSP and explained in which steps are used in the ACO solution. With simulation study, it is aimed to show, how ACO copes with the JSSP clearly.

The new challenges of "Agile Manufacturing" and distributed decision making entailed by decentralized organizations led to our interest in the study of computational cooperative problem solving models and coordination techniques for distributed production management. The goal of our research is to address the technical need of distributed production management and develop appropriate computational approaches to support adaptive, cost-effective responsiveness. In particular, we focus on the challenging problem of job shop scheduling, which has been one of the primary foci of production scheduling research. This paper presents a multi-agent problem solving model and an effective coordination technique for job shop scheduling. The model involves a group of agents; each agent is associated with either a job or a resource. A solution to a production scheduling problem is the result of coordinated conflict resolution in the iterative and asynchronous multi-agent decision making process. It is well known in distributed systems research that for tightly interacting, non-decomposable problems, such as job shop scheduling, the need for communicating partial solution results among parts of the system rapidly degrades system performance. On the other hand, limiting communication degrades solution quality. One can limit communication by employing shared memory, but this has the drawback that the shared memory becomes a bottleneck and, in addition, using shared memory limits decentralization. In our approach, we judiciously balance the above concerns. We limit interagent communication through a scheme that employs efficient, small and distributed shared memories, each of which is associated with and shared by a limited number of agents. We also exploit problem characteristics (e.g. disparity among subproblems) to design an effective coordination technique for the job shop scheduling problem. We have evaluated the utility of our approach through extensive experimentation on a variety of job shop constraint satisfaction and optimization problems with different optimization objectives. Our results show that our approach outperforms or gives comparative results with other state-of-the-art scheduling techniques on benchmark problems.

This paper presents a simulated annealing search procedure developed to solve job shop scheduling problems simultaneously subject to tardiness and inventory costs. The procedure is shown to significantly increase schedule quality compared to multiple combinations of dispatch rules and release policies, though at the expense of intense computational efforts. A meta-heuristic procedure is developed that aims at increasing the efficiency of simulated annealing by dynamically inflating the costs associated with major inefficiencies in the current solution. Three different variations of this procedure are considered. One of these variations is shown to yield significant reductions in computation time, especially on problems where search is more likely to get trapped in local minima. We analyze why this variation of the meta-heuristic is more effective than the others.

A guided local search with iterative ejections of bottleneck operations for the job shop scheduling problem

This paper presents a two-stage genetic algorithm (2S-GA) for multi-objective Job Shop scheduling problems. The 2S-GA is proposed with three criteria: Minimize makespan, Minimize total weighted earliness, and Minimize total weighted tardiness. The proposed algorithm is composed of two Stages: Stage 1 applies parallel GA to find the best solution of each individual objective function with migration among populations. In Stage 2 the populations are combined. The evolution process of Stage 2 is based on Steady-State GA using the weighted aggregating objective function. The algorithm developed can be used with one or two objectives without modification. The genetic algorithm is designed and implemented with the GALIB object library. The random keys representation is applied to the problem. The schedules are constructed using a permutation with m-repetitions of job numbers. Performance of the proposed algorithm is tested on published benchmark instances and compared with results from other published approaches for both the single objective and multi-objective cases. The experimental results show that 2S-GA is effective and efficient to solve job shop scheduling problem in term of solution quality.

This paper describes a population-based approach that uses a honey bees foraging model to solve job shop scheduling problems. The algorithm applies an efficient neighborhood structure to search for feasible solutions and iteratively improve on prior solutions. The initial solutions are generated using a set of priority dispatching rules. Experimental results comparing the proposed honey bee colony approach with existing approaches such as ant colony, tabu search and shifting bottleneck procedure on a set of job shop problems are presented. The results indicate the performance of the proposed approach is comparable to other efficient scheduling approaches.

The most basic concept of (meta-)heuristic optimization is to prefer better solutions over worse ones. Algorithms utilizing Frequency Fitness Assignment (FFA) break with this idea and instead move towards solutions whose objective value has been encountered less often so far. We investigate whether this approach can be applied to solve the classical Job Shop Scheduling Problem (JSSP) by plugging FFA into the (1+1)-EA, i.e., the most basic local search. As representation, we use permutations with repetitions. Within the budget chosen in our experiments, the resulting (1+1)-FEA can obtain better solutions in average on the Fisher-Thompson, Lawrence, Applegate-Cook, Storer-Wu-Vaccari, and Yamada-Nakano benchmark sets, while performing worse on the larger Taillard and Demirkol-Mehta-Uzsoy benchmarks. We find that while the simple local search with FFA does not outperform the pure algorithm, it can deliver surprisingly good results, especially since it is not directly biased towards searching for them.

The theoretical research literature on Manufacturing Schedule Optimisation has yielded few applications of dynamic schedule optimisation to actual manufacturing situations. This is particularly true of the classical job-shop scheduling model. Conventional job-shop scheduling problem (JSP) approaches were found to be based on an over simplified assumption set and a single optimisation objective. Most JSP algorithms neglect issues such as sequence-dependent set-ups, and parallel resources. An actual job-shop scheduling problem in a case study firm is used to identify the deficiencies in the traditional JSP solution approaches. A more realistic scheduling model was formulated and addressed using Constraint Satisfaction Problem (CSP) formulation and heuristic techniques. This combined heuristic and CSP approach was found in practice to be able to deal with an industrial scale problem and achieved superior plant performance in terms of order tardiness and machine utilisation. This approach represented much more effectively the real world manufacturing problem than the conventional approach.

Scheduling for a job shop production system is an integral aspect of production management. Scheduling operations must minimize stock, waste, and idle time and ensure on-time delivery of goods in a time window problem. In this study, due date is considered as an interval instead of a time point. This study addresses scheduling with a time window of job shop scheduling problem (JSP) and yields a solution that is close to the time window. The total penalty due to earliness and tardiness is minimized. As the problem is NP-hard, a mathematical model of the JSP with a time window is initially constructed, and data are then simulated. Solutions are obtained by ant colony optimization (ACO) programs written in C-language and are compared with the best solution obtained using LINGO 7.0 to determine the efficiency and robustness. Test results indicate that ACO is extremely efficient. Solution time using ACO is less than that using LINGO. Hence, ACO is both effective and efficient, which are two qualities stressed in business management.