Stochastic mixed model sequencing with multiple stations using reinforcement learning and probability quantiles

In this study, we propose a reinforcement learning (RL) approach for minimizing the number of work overload situations in the mixed model sequencing (MMS) problem with stochastic processing times. The learning environment simulates stochastic processing times and penalizes work overloads with negative rewards. To account for the stochastic component of the problem, we implement a state representation that specifies whether work overloads will occur if the processing times are equal to their respective 25%, 50%, and 75% probability quantiles. Thereby, the RL agent is guided toward minimizing the number of overload situations while being provided with statistical information about how fluctuations in processing times affect the solution quality. To the best of our knowledge, this study is the first to consider the stochastic problem variation with a minimization of overload situations.

A simheuristic approach for the flexible job shop scheduling problem with stochastic processing times

In this work, we address the flexible job shop scheduling problem (FJSSP), which is a classification of the well-known job shop scheduling problem. This problem can be encountered in real-life applications such as automobile assembly, aeronautical, textile, and semiconductor manufacturing industries. To represent inherent uncertainties in the production process, we consider stochastic flexible job shop scheduling problem (SFJSSP) with operation processing times represented by random variables following a known probability distribution. To solve this stochastic combinatorial optimization problem we propose a simulation-optimization approach to minimize the expected makespan. Our approach employs Monte Carlo simulation integrated into a Jaya algorithm framework. Due to the unavailability of standard benchmark instances in SFJSSP, our algorithm is evaluated on an extensive set of well-known FJSSP benchmark instances that are extended to SFJSSP instances. Computational results demonstrate the performance of the algorithm at different variability levels through the use of reliability-based methods.

JOB-SHOP SCHEDULING OPTIMIZATION WITH STOCHASTIC PROCESSING TIMES

In this study, a job shop scheduling optimization model under risk has been developed to minimize the make span. This model has been built using Microsoft Excel spreadsheets and solved using @Risk solver. A set of experiments have been also conducted to examine the accuracy of the model and its effectiveness has been proven.

Evaluation of pull production control strategies under uncertainty: An integrated fuzzy AHP-TOPSIS approach

Purpose: Just-In-Time (JIT) production has continuously been considered by industrial practitioners and researchers as a leading strategy for the yet popular Lean production. Pull Production Control Policies (PPCPs) are the major enablers of JIT that locally control the level of inventory by authorizing the production in each station. Aiming to improve the PPCPs, three authorization mechanisms: Kanban, constant-work-in-process (ConWIP), and a hybrid system, are evaluated by considering uncertainty.Design/methodology/approach: Multi-Criteria Decision Making (MCDM) methods are successful in evaluating alternatives with respect to several objectives. The proposed approach of this study applies the fuzzy set theory together with an integrated Analytical Hierarchy Process (AHP) and a Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) method.Findings: The study finds that hybrid Kanban-ConWIP pull production control policies have a better performance in controlling the studied multi-layer multi-stage manufacturing and assembly system.Practical implications: To examine the approach a real case from automobile electro mechanical part production industry is studied. The production system consists of multiple levels of manufacturing, feeding a multi-stage assembly line with stochastic processing times to satisfy the changing demand.Originality/value: This study proposes the integrated Kanban-ConWIP hybrid pull control policies and implements several alternatives on a multi-stage and multi-layer manufacturing and assembly production system. An integrated Fuzzy AHP TOPSIS method is developed to evaluate the alternatives with respect to several JIT criteria.

Strategic WIP Inventory Positioning for Make-to-Order Production with Stochastic Processing Times

It is vital for make-to-order manufacturers to shorten the lead time to meet the customers’ requirements. Holding work-in-process (WIP) inventory at more stations can reduce the lead time, but it also brings about higher inventory holding cost. Therefore, it is important to seek out the optimal set of stations to hold WIP inventory to minimize the total inventory holding cost, while meeting the required due date for the final product at the same time. Since the problem with deterministic processing times at the stations has been addressed, as a natural extension, in this study, we address the problem with stochastic processing times, which is more realistic in the manufacturing environment. Assuming that the processing times follow normal distributions, we propose a solution procedure using genetic algorithm.