Acceptance sampling plans are practical tools for quality assurance applications involving quality contract on product orders. The sampling plans provide the vendor and buyer decision rules for product acceptance to meet the preset product quality requirement. As the rapid advancement of manufacturing technology, suppliers require their products to be of high quality with very low fraction of defectives often measured in parts per million. Unfortunately, traditional methods for calculating fraction of defectives no longer work since any sample of reasonable size probably contains no defective product items. In this paper, we introduce an effective sampling plan based on process capability index Cpk to deal with product acceptance determination for low fraction of defectives. The proposed new sampling plan is developed based on the exact sampling distribution rather than approximation. Practitioners can use the proposed method to determine the number of required inspection units, the critical acceptance value, and make reliable decisions in product acceptance.

An effective decision making method for product acceptance

Climate change mitigation, the goal of reducing CO2 emissions, more stringent regulations and the increment in energy costs have pushed researchers to study energy efficiency and renewable energy sources. Manufacturing systems are large energy consumers and are thus responsible for huge greenhouse gas emissions; for these reasons, many studies have focused on this topic recently. This review aims to summarize the most important papers on energy efficiency and renewable energy sources in manufacturing systems published in the last fifteen years. The works are grouped together, considering the system typology, i.e., manufacturing system subclasses (single machine, flow shop, job shop, etc.) or the assembly line, the developed energy-saving policies and the implementation of the renewable energy sources in the studied contexts. A description of the main approaches used in the analyzed papers was discussed. The conclusion reports the main findings of the review and suggests future directions for the researchers in the integration of renewable energy in the manufacturing systems consumption models.

A Literature Review of Energy Efficiency and Sustainability in Manufacturing Systems

A green scheduling algorithm for the distributed flowshop problem

The quest for sustainability has arrived to the manufacturing world, with the emergence of a research field known as green scheduling. Traditional performance objectives now co-exist with energy-saving ones. In this work, we tackle a job shop scheduling problem with the double goal of minimising energy consumption during machine idle time and minimising the project’s makespan. We also consider uncertainty in processing times, modelled with fuzzy numbers. We present a multi-objective optimisation model of the problem and we propose a new enhanced memetic algorithm that combines a multiobjective evolutionary algorithm with three procedures that exploit the problem-specific available knowledge. Experimental results validate the proposed method with respect to hypervolume, ϵ -indicator and empirical attaintment functions.

Multi-objective enhanced memetic algorithm for green job shop scheduling with uncertain times

The quest for sustainability has arrived to the manufacturing world, with the emergence of a research field known as green scheduling. Traditional performance objectives now co-exist with energy-saving ones. In this work, we tackle a job shop scheduling problem with the double goal of minimising energy consumption during machine idle time and minimising the project’s makespan. We also consider uncertainty in processing times, modelled with fuzzy numbers. We present a multi-objective optimisation model of the problem and we propose a new enhanced memetic algorithm that combines a multiobjective evolutionary algorithm with three procedures that exploit the problem-specific available knowledge. Experimental results validate the proposed method with respect to hypervolume, ϵ-indicator and empirical attaintment functions. 

An energy-efficient bi-objective no-wait permutation flowshop scheduling problem to minimize total tardiness and total energy consumption

No-wait flowshop scheduling problem (NWFSP) is a well-known strongly NP-hard problem, where in-process waiting is not allowed between any two consecutive machines in such a way that once a job is started, subsequent processing must be carried out on all machines until completion. In this paper, we propose an energy-efficient NWFSP in order to investigate the trade-off between makespan and total energy consumption. The energy-efficient NWFSP aims to seek to obtain Pareto solution sets to minimize the makespan and the total energy consumption conflicting with each other. Unlike the classical NWFSP, there are different speed levels for each job on machines and the processing times of jobs can differ according to the assigned speed levels. Therefore, we modify the formulation of NWFSP by introducing a speed scaling strategy in order to approximate Pareto solution sets, i.e., non-dominated solution sets. In this paper, we propose a mixed-integer linear programming model (MILP), an energy-efficient variable block insertion heuristic (EE-VBIH), an energy-efficient iterated greedy algorithm (IG) and an energy-efficient & IG-ALL) to solve the energy-efficient NWFSP. Extensive computational analyses on Taillard’s benchmark suite show that the proposed algorithms are very effective for approximating Pareto solution sets.

Metaheuristics for Energy-Efficient No-Wait Flowshops: A Trade-off Between Makespan and Total Energy Consumption

A circular food supply chain network model to reduce food waste

A Discrete Artificial Bee Colony Algorithm for the Energy-Efficient No-Wait Flowshop Scheduling Problem

This paper studies workforce assignment problem for battery production in a company in Turkey. Several types of batteries are produced in the studied company. Mostly, the operations are semi-automated. In the production process, the workers are assigned to multiple operations irregularly based on the priority of productions. In the company, average utilization of worker is low, and average cycle time of a product is high due to inefficient allocation of the workforce within the operations. In order to analyze the main system problem, we simulate the system and observe the queue lengths to identify the bottlenecks. By dynamic assignment of workers at stations based on real time queue conditions, the workloads can be balanced throughout the production lines. In this project, a simulation-based system improvement is completed by applying: (i) dynamic utilization of workforce to reduce average cycle time of a battery, (ii) assignment of parallel workforce where they can work for the same operation simultaneously, and (iii) observation of real-time queue lengths of stations. Three dynamic assignment policies are developed and compared with each other. The best policy providing minimum cycle time for a battery production is selected to be the best.

Damla Yüksel

Papers

An energy-efficient bi-objective no-wait permutation flowshop scheduling problem to minimize total tardiness and total energy consumption

Metaheuristics for Energy-Efficient No-Wait Flowshops: A Trade-off Between Makespan and Total Energy Consumption

A circular food supply chain network model to reduce food waste

A Discrete Artificial Bee Colony Algorithm for the Energy-Efficient No-Wait Flowshop Scheduling Problem

Parallel Workforce Assignment Problem for Battery Production