Workload based order acceptance in job shop environments

Abstract

In practice, order acceptance and production planning are often functionally separated. As a result, order acceptance decisions are made without considering the actual workload in the production system, or by only regarding the aggregate workload. We investigate the importance of a good workload based order acceptance method in over-demanded job shop environments, and study approaches that integrate order acceptance and resource capacity loading. We present sophisticated methods that consider technological restrictions, such as precedence relations, and release and due dates of orders. We use a simulation model of a generic job shop to compare these methods with straightforward methods, which consider capacity restrictions at an aggregate level and ignore precedence relations. We compare the performance of the approaches based on criteria such as capacity utilisation. The simulation results show that the sophisticated approaches significantly outperform the straightforward approaches in case of tight due dates (little slack). In that case, improvements of up to 30% in utilisation rate can be achieved. In case of much slack, a sophisticated order acceptance method is less important.

An agent-based model to support negotiation-based order acceptance in a multi-plant enterprise

“…If the order could be inserted in a feasible schedule, a schedule that minimizes the sum of setup and holding costs was selected. Ebben et al (2005) examined the importance of a proper workload-based order acceptance in an over-loaded job shop environments. They studied several methods, which were dierent on the level of required details that integrate OA and resource capacity planning.…”

An order acceptance using FAHP and TOPSIS methods: A case study of Iranian vehicle belt production industry

“…We try to select the best orders received by the company by virtue of the organizational goals and the low capacity because of the increased rate of the orders. First, the decision group needs to be formed and the orders of the producing sections are ignored in the decision process (Ebben et al, 2005). We try to employ the personnel of different producing sections with the orders reception section employees in defining the group members in order to consider the production limits during accepting or refusing the orders.…”

“…Sometimes the levels of incoming orders increase to levels beyond the available capacity. Managers initially attempt to seek extra capacity using tactics such as overtime, outsourcing etc to compensate for all or part of the missing capacity (Ebben et al, 2005;Zhang & Shaofu, 2010). Ultimately, by considering the total capacity (the sum of available capacity and extra capacities obtained via the mentioned tactics) against the required capacity a company will have to decline some of its clients' orders if faced with capacity shortage.…”

“…The literature on order acceptance has grown rapidly over the past decade (Slotnick & Morton, 2007). Thus, researchers have attempted to find an optimum system to either accept or reject orders using multitudes of techniques such as Dynamic Programming (Alidaee et al, 2001;Lewis & Slotnick, 2002;Herbots et al, 2007), Mathematics Programming (Guerrero & Kern, 1990;Slotnick & Morton, 1996;Slotnick & Morton, 2007), Simulation (Ten kate, 1994;Ten kate, 1995;Nandi & Rogers, 2003;Nandi & Rogers, 2004;Ebben et al, 2005), Decision theory (Balakrishnan et al,1996;Balakrishnan et al,1999), Heuristics (Ghosh, 1997;Defregger & Kuhn, 2004;Hing et al, 2007), Genetic algorithm (Roundy et al, 2005;Rom & Slotnick, 2009), Neural networks Hing et al, 2002), Neuro-genetics (Snoek, 2000), Markov decision (Kniker & Burman, 2001) etc. References Slotnick and Morton (2007), Ghosh (1997), Jalora (2006) and Ivanescu (2004) provide more detailed information.…”

A branch and price solution approach for order acceptance and capacity planning in make-to-order operations

“…A feasible schedule for job j should satisfy the processing time constraint (3-3), the physical constraint of processing job j for not more than l ts hours in source s of time period t, the due-date constraint (3-7) and the precedence constraints (3)(4)(5)(6)(7)(8) and (3)(4)(5)(6)(7)(8)(9). The corresponding formulation for the sub-problem or pricing problem of job j will consist of the constraint set (3-3) to (3-11) with an objective of minimizing the cost of processing job j by its due-date.…”