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This research is motivated by the need for considering lot sizing while accepting customer orders in a make-to-order (MTO) environment, in which each customer order must be delivered by its due date. Job shop is the typical operation model used in an MTO operation, where the production planner must make three concurrent decisions; they are order selection, lot size, and job schedule. These decisions are usually treated separately in the literature and are mostly led to heuristic solutions. The first phase of the study is focused on a formal definition of the problem. Mathematical programming techniques are applied to modeling this problem in terms of its objective, decision variables, and constraints. A commercial solver, CPLEX is applied to solve the resulting mixed-integer linear programming model with small instances to validate the mathematical formulation. The computational result shows it is not practical for solving problems of industrial size, using a commercial solver. The second phase of this study is focused on development of an effective solution approach to this problem of large scale. The proposed solution approach is an iterative process involving three sequential decision steps of order selection, lot sizing, and lot scheduling. A range of simple sequencing rules are identified for each of the three sub-problems. Using computer simulation as the tool, an experiment is designed to evaluate their performance against a set of system parameters. For order selection, the proposed weighted most profit rule performs the best. The shifting bottleneck and the earliest operation finish time both are the best scheduling rules. For lot sizing, the proposed minimum cost increase heuristic, based on the Dixon-Silver method performs the best, when the demand-to-capacity ratio at the bottleneck machine is high. The proposed minimum cost heuristic, based on the Wagner-Whitin algorithm is the best lot-sizing heuristic for shops of a low demand-to-capacity ratio. The proposed heuristic is applied to an industrial case to further evaluate its performance. The result shows it can improve an average of total profit by 16.62%. This research contributes to the production planning research community with a complete mathematical definition of the problem and an effective solution approach to solving the problem of industry scale.

no / No / Scheduling systems based on traditional heuristic rules, which deal with the complexities of manufacturing systems, have been used by researchers for the past six decades. These heuristics rules prioritise all jobs that are waiting to be processed on a resource. In this paper, a novel Index Based Heuristic (IBH) solution for the Job Shop Scheduling Problem (JSSP) is presented with the objective of minimising the overall Makespan (Cmax). The JSSP is still a challenge to researchers and is far from being completely solved due to its combinatorial nature. JSSP suits the challenges of current manufacturing environments. The proposed IBH calculates the indices of candidate jobs and assigns the job with the lower index value to the available machine. To minimise the gap between jobs, a swap technique is introduced. The swap technique takes candidate jobs for a machine and swaps them without violating the precedence constraint. Several benchmark problems are solved from the literature to test the validity and effectiveness of the proposed heuristic. The results show that the proposed IBH based algorithm outperforms the traditional heuristics and is a valid methodology for JSSP optimization.

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Studien genomfördes på industriföretaget Gränges Finspång AB som är en producent av valsade aluminiumprodukter för värmeväxlare vilka används som komponenter främst inom bilindustrin och värme, ventilation och luftkonditionering. Aluminium är en miljöeffektiv råvara tack vare materialets naturliga egenskaper samt dess återanvändbarhet vilket har lett till att allt fler företag vill ta vara på dessa egenskaper vid tillverkning av klimatsmarta produkter. För Gränges Finspång AB har materialets aktualitet på marknaden inneburit en ökad efterfrågan på företagets produkter vilket i sin tur har satt ökad press på företagets produktionseffektivitet. Den produktionsprocess som studerades på företaget var en uppsättning maskiner – även kallade kallvalsverk – vilka kan liknas med en funktionell verkstad. Syftet med studien var att, med hjälp av optimeringsmetoder, ta fram en modell som kan användas som beslutsunderlag för sekvensering av produkter i företagets kallvalsverk. Utifrån intervjuer, granskning av interna dokument och en kvantitativ dataanalys genomfördes en kartläggning av Gränges Finspång AB:s hela produktionsflöde såväl som de processer unika för kallvalsprocessen. För sekvensering av företagets produkter tillämpades en linjär heltalsmodell vilken anger optimum för maximalt 14 produkter. Studien bekräftar att företagets kallvalsning är ett komplext produktionssystem ur ett schemaläggningsperspektiv. / <p>Examensarbetet är utfört vid Institutionen för teknik och naturvetenskap (ITN) vid Tekniska fakulteten, Linköpings universitet</p>

This thesis aims to review and analyze the scheduling problem in general and Job Shop Scheduling Problem (JSSP) in particular and the solution techniques applied to these problems. The JSSP is the most general and popular hard combinational optimization problem in manufacturing systems. For the past sixty years, an enormous amount of research has been carried out to solve these problems. The literature review showed the inherent shortcomings of solutions to scheduling problems. This has directed researchers to develop hybrid approaches, as no single technique for scheduling has yet been successful in providing optimal solutions to these difficult problems, with much potential for improvements in the existing techniques. The hybrid approach complements and compensates for the limitations of each individual solution technique for better performance and improves results in solving both static and dynamic production scheduling environments. Over the past years, hybrid approaches have generally outperformed simple Genetic Algorithms (GAs). Therefore, two novel priority heuristic rules are developed: Index Based Heuristic and Hybrid Heuristic. These rules are applied to benchmark JSSP and compared with popular traditional rules. The results show that these new heuristic rules have outperformed the traditional heuristic rules over a wide range of benchmark JSSPs. Furthermore, a hybrid GA is developed as an alternate scheduling approach. The hybrid GA uses the novel heuristic rules in its key steps. The hybrid GA is applied to benchmark JSSPs. The hybrid GA is also tested on benchmark flow shop scheduling problems and industrial case studies. The hybrid GA successfully found solutions to JSSPs and is not problem dependent. The hybrid GA performance across the case studies has proved that the developed scheduling model can be applied to any real-world scheduling problem for achieving optimal or near-optimal solutions. This shows the effectiveness of the hybrid GA in real-world scheduling problems. In conclusion, all the research objectives are achieved. Finaly, the future work for the developed heuristic rules and the hybrid GA are discussed and recommendations are made on the basis of the results.

This research proposes a method to solve the adaptive, multi-objective job shop scheduling problem. Adaptive scheduling is necessary to deal with internal and external disruptions faced in real life manufacturing environments. Minimizing the mean tardiness for jobs to effectively meet customer due date requirements and minimizing mean flow time to reduce the lead time jobs spend in the system are optimized simultaneously. An asexual reproduction genetic algorithm with multiple mutation strategies is developed to solve the multi-objective optimization problem. The model is tested for single day and multi-day adaptive scheduling. Results are compared with those available in the literature for standard problems and using priority dispatching rules. The findings indicate that the genetic algorithm model can find good solutions within short computational time.

Production scheduling is one of the most important issues in the planning and operation of manufacturing systems. Customers increasingly expect to receive the right product at the right price at the right time. Various problems experienced in manufacturing, for example low machine utilization and excessive work-in-process, can be attributed directly to inadequate scheduling. In this dissertation a production scheduling algorithm is developed for Optimatix, a South African-based company specializing in supply chain optimization. To address the complex requirements of the customer, the problem was modeled as a flexible job shop scheduling problem with sequence-dependent set-up times, auxiliary resources and production down time. The algorithm development process focused on investigating the application of both particle swarm optimization (PSO) and differential evolution (DE) to production scheduling environments characterized by multiple machines and multiple objectives. Alternative problem representations, algorithm variations and multi-objective optimization strategies were evaluated to obtain an algorithm which performs well against both existing rule-based algorithms and an existing complex flexible job shop scheduling solution strategy. Finally, the generality of the priority-based algorithm was evaluated by applying it to the scheduling of production and maintenance activities at Centurion Ice Cream and Sweets. The production environment was modeled as a multi-objective uniform parallel machine shop problem with sequence-dependent set-up times and unavailability intervals. A self-adaptive modified vector evaluated DE algorithm was developed and compared to classical PSO and DE vector evaluated algorithms. Promising results were obtained with respect to the suitability of the algorithms for solving a range of multi-objective multiple machine scheduling problems. Copyright / Dissertation (MEng)--University of Pretoria, 2009. / Industrial and Systems Engineering / unrestricted