Template-Type: ReDIF-Article 1.0 Author-Name: Goutham Ramaraj Author-X-Name-First: Goutham Author-X-Name-Last: Ramaraj Author-Name: Zhengyang Hu Author-X-Name-First: Zhengyang Author-X-Name-Last: Hu Author-Name: Guiping Hu Author-X-Name-First: Guiping Author-X-Name-Last: Hu Title: A two-stage stochastic programming model for production lot-sizing and scheduling under demand and raw material quality uncertainties Abstract: Production planning and scheduling focus on efficient use of resources and are widely used in the manufacturing industry, especially when the system operates in an uncertain environment. The goal of this paper is to provide a two-stage stochastic programming framework for a multi-period, multi-product, lot-sizing and scheduling problem considering uncertainties in both demand and the quality of raw materials. The objectives are to determine the number of units to be produced and the production sequence so that the total production costs are minimised. The decisions made in the first stage include the basic production plan along with the production quantities and sequences, which are later updated with recourse decisions on overtime production made in the second-stage. To demonstrate the proposed decision-making framework, a case study for a manufacturing facility producing braking equipment for the automotive industry was conducted. The results show that the stochastic model is more effective in production planning under the uncertainties considered. The managerial insights derived from this study will facilitate the decision making for determining optimal production quantities and sequences under uncertainties. Journal: Int. J. of Planning and Scheduling Pages: 1-27 Issue: 1 Volume: 3 Year: 2019 Keywords: stochastic programming; production planning; uncertainty; scheduling; lot-sizing; scenario generation; scenario reduction. File-URL: http://www.inderscience.com/link.php?id=102993 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijps:v:3:y:2019:i:1:p:1-27 Template-Type: ReDIF-Article 1.0 Author-Name: Shiyang Huang Author-X-Name-First: Shiyang Author-X-Name-Last: Huang Author-Name: Guiping Hu Author-X-Name-First: Guiping Author-X-Name-Last: Hu Title: Automated guided vehicle dispatching based on combinatorial optimisation to minimise job waiting time on shop floors Abstract: On manufacturing shop floors, automated guided vehicles (AGVs) have been widely adopted and configured for material handling, and they are dispatched to transport jobs between work centres. In this paper, two AGV dispatching strategies based on combinatorial optimisation are proposed. AGVs are assigned to work centres according assignment optimisation models to minimise the total waiting time of jobs. In the decision horizon, status of AGVs and jobs in or between work centres are predicted. The AGV dispatching strategies take future transportation requests into consideration and optimally configure transportation resources, such that material handling can be more efficient than those adopting classic AGV assignment rules in which only the current request is considered. The strategies were demonstrated in a case study and compared with classic AGV assignment rules including random assignment and nearest vehicle/shortest travel time rule. The results showed that the proposed dispatching strategies could better control job waiting time on the shop floors compared to classic AGV assignment rules. Journal: Int. J. of Planning and Scheduling Pages: 28-46 Issue: 1 Volume: 3 Year: 2019 Keywords: AGV dispatching; combinatorial optimisation; job waiting time minimisation; assignment problem. File-URL: http://www.inderscience.com/link.php?id=103016 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijps:v:3:y:2019:i:1:p:28-46 Template-Type: ReDIF-Article 1.0 Author-Name: Burak Cankaya Author-X-Name-First: Burak Author-X-Name-Last: Cankaya Author-Name: Ezra Wari Author-X-Name-First: Ezra Author-X-Name-Last: Wari Author-Name: Berna Eren Tokgoz Author-X-Name-First: Berna Eren Author-X-Name-Last: Tokgoz Title: A chemical tanker scheduling problem: Port of Houston case study Abstract: The objective of this study was to develop scheduling solutions for chemical vessels in a port. Chemical tankers carry multiple liquid cargos and have to visit multiple terminals to load/unload these cargos. The practice to date in scheduling these loading/unloading activities for the Port of Houston (PoH) has primarily involved manual methods, which result in several inefficiencies such as long waiting times and inefficient resource utilisation. This paper proposes three methods for tanker-scheduling problems, formulated as an open-shop scheduling problem with sequence-dependent post-set-up times: a mixed-integer programming (MIP); a constraint programming (CP); and a dispatching rule, longest processing time (LPT) scheduling heuristic. Overall, the results showed the MIP method to be a more suitable tool for hourly and daily scheduling, whereas the CP and the LPT heuristic proved suitable for longer-horizon scheduling problems (weekly, monthly, or semi-annual) and for use as real-time scheduling tools. Journal: Int. J. of Planning and Scheduling Pages: 47-67 Issue: 1 Volume: 3 Year: 2019 Keywords: ship scheduling; berth allocation problem; sequence-dependent set-up; mixed-integer programming; MIP; constraint programming; CP; dispatching rule; open shop heuristics. File-URL: http://www.inderscience.com/link.php?id=103029 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijps:v:3:y:2019:i:1:p:47-67 Template-Type: ReDIF-Article 1.0 Author-Name: Javier F. Urrutia Author-X-Name-First: Javier F. Author-X-Name-Last: Urrutia Author-Name: Lorena Pradenas Author-X-Name-First: Lorena Author-X-Name-Last: Pradenas Title: An aggregate production planning mathematical model, under a peak-demand electrical control policy Abstract: The impact of energy costs on the production planning decisions of most manufacturing companies is certainly crucial. For electrical consumption, there is a link between production activities and the regions energy policies, for example where electricity price variations are a tactical problem to be solved via equipment sequencing to address the known electricity costs. The goal of this study is to apply an aggregate production planning mathematical model, under a peak-demand electrical control policy, for an energy intensive manufacturer of grinding media, in Chile. The objective function is to maximise the profit of a company in a horizon time of T periods, where a penalty is incurred when production lines are used at peak hours (per electric market regulations/contracts). Furthermore, the model determines the optimum period for a major preventive maintenance for each of its process lines. The case studied is a plant which produces steel balls that are used for mineral grinding; the plant manufactures ten types steel balls from round steel bars (different diameters of balls/bar), a process with high energy demand (induction bar heating). The proposed model was implemented in the Lingo software, allowing for a consistent aggregate production planning to maximising the company's profits. Journal: Int. J. of Planning and Scheduling Pages: 68-83 Issue: 1 Volume: 3 Year: 2019 Keywords: steel-ball manufacturing; electrical demand control policies; mathematical programming; energy demand manufacturing; mixed integer programming; production planning and scheduling. File-URL: http://www.inderscience.com/link.php?id=103034 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijps:v:3:y:2019:i:1:p:68-83