DISCRETE PARTICLE SWARM OPTIMIZATION FOR THE ORIENTEERING PROBLEM

Discrete particle swarm optimization (DPSO) is gaining popularity in the area of combinatorial optimization in the recent past due to its simplicity in coding and consistency in performance.  A DPSO algorithm has been developed for orienteering problem (OP) which has been shown to have many practical applications.  It uses reduced variable neighborhood search as a local search tool.  The DPSO algorithm was compared with ten heuristic models from the literature using benchmark problems.  The results show that the DPSO algorithm is a robust algorithm that can optimally solve the well known OP test problems

An approach for scheduling a bi-objective flexible flow shop

This work considers a hybrid flow shop scheduling problem, a well-known production systems problem. It has been largely studied in the literature as a single objective optimization problem. We analyze a flow shop with two stages and ms identical and unrelated parallel machines at each stage s. Given a set of jobs with their process times, the objective is to schedule the jobs such that both the makespan and the maximum tardiness are minimized. Each job is to be processed on one of the first-stage parallel machines, and then on the second-stage parallel machines. The problem under study is NP-hard and it can be represented as FF2(Pm1,Pm2||w∗Cmax+(1-w)∗ Tmax). When m1=m2= 1 and the objective is only minimize the makespan, our problem reduces to F2||Cmax. A particle swarm optimization (PSO) approach is proposed to solve the problem. In the experimental phase, instances of 5, 10, 20, 50 and 100 jobs were run. The solution quality and run time of PSO is compared with a commercial solver used to solve the mathematical formulation. Experimental study clearly highlights the advantages, in terms of solution quality and run time, of using PSO to solve large-scale problems

Makespan minimization in a job shop with a BPM using simulated annealing

A scheduling problem commonly observed in the metal working industry has been studied in this research effort -- A job shop equipped with one batch processing machine (BPM) and several unit-capacity machines has been considered. Given a set of jobs, their process routes,processing requirements, and size, the objective is to schedule the jobs such that the makespan is minimized -- The BPM can process a batch of jobs as long as its capacity is not exceeded -- The batch processing time is equal to the longest processing job in the batch -- If no batches were to be formed, the scheduling problem under study reduces to the classicaljob shop problem with makespan objective, which is known to be nondeterministic polynomial time-hard -- A network representation of the problem using disjunctive and conjunctive arcs, and a simulated annealing (SA) algorithm are proposed to solve the problem. The solution quality and run time of SA are compared with CPLEX, a commercial solver used to solve the mathematical formulation and with four dispatching rules -- Experimental study clearly highlights the advantages, in terms of solution quality and run time, of using SA to solve large-scale problem

Assessment of Factors Influencing Agility in Start-Ups Industry 4.0

Agility has a special place in the start-up Industry 4.0 era. More research is required to properly comprehend the agile implications of start-up Industry 4.0 regarding the impact of digitization on the economy, the environment, and society. Investigating the effects of start-up 4.0 agility is still in its early stages. The current study simulates the variables impacting agility in start-up activities in Industry 4.0 to tackle this problem. In addition to the pre-arranged interview, a closed-ended questionnaire was used to gather information. In the context of start-up operations 4.0, the MICMAC technique is used to evaluate and categorize the components that contribute to agility in order to comprehend their interconnections. The research identified eleven characteristics of facilitating agility in start-up operations 4.0. Industry 4.0 concepts have significantly influenced large organizations but deploying agility in start-up 4.0 has been less visible. Hence, this study presents an innovative approach to incorporating agility in modern start-up operations. The significance of artificial intelligence, cloud computing, network and connectivity, technology, and digital twin in this context is evident. The research provides important light on the elements that contribute to the successful use of agility in start-up 4.0, offering useful insights for stakeholders and academics

GRASP Algorithm to Minimize Makespan in a No-Wait Flow Shop with Batch Processing Machines

Given a set of jobs and two Batch Processing Machines (BPMs) in a flow shop environmentthe objective is to batch the jobs and sequence the batches such that makespan is minimized

Scheduling a Job Shop with a BPM Using Simulated Annealing. Proceedings of Industrial Engineering Research Conference (ISERC)

We consider a scheduling problem commonly observed in the metal working industry. We analyze a job shop which is equipped with one batch processing machine (BPM)and several unit-capacity machines

Minimizing Makespan in a No-Wait Flow Shop With Batch Processing Machines

Given a set of jobs and two batch processing machines (BPMs) arranged in a flow shop environmentthe objective is to batch the jobs and sequence the batches such that the makespan is minimized

Minimizing makespan in a two-machine no-wait flow shop with batch processing machines

Given a set of jobs and two batch processing machines (BPMs) arranged in a flow shop environment,the objective is to batch the jobs and sequence the batches such that the makespan is minimized. The job sizes, ready times, and processing times on the two BPMs are knowN -- The batch processing machines can process a batch of jobs as long as the total size of all the jobs assigned to a batch does not exceed its capacity -- Once the jobs are batched, the processing time of the batch on the first machine is equal to the longest processing job in the batch; processing time of the batch on the second machine is equal to the sum of processing times of all the jobs in the batch -- The batches cannot wait between two machines (i.e., no-wait) -- The problem under study is NP-hard -- We propose a mathematical formulation and present a particle swarm optimization (PSO) algorithm -- The solution quality and run time of PSO is compared with a commercial solver used to solve the mathematical formulation Experimental study clearly highlights the advantages, in terms of solution quality and run time, of using PSO to solve large-scale problem