Search

This study uses an agent-based approach with a combinatorial auction mechanism to solve the joint order acceptance and assembly job shop scheduling problem. A set of jobs is offered. Each job has a revenue, ready time, due date, deadline, and consists of a set of operations with precedence relationships. Jobs that deviate from their due dates incur earliness/tardiness penalties. An operation may require several units of capacity per time unit and a resource could have multiple units of capacity. The manufacturer can reject any job to satisfy the capacity constraints and maximise the overall profit. We develop a mathematical model for the problem, then use an agent-based approach to solve it. First, the relaxed problem is decomposed into a set of job-level subproblems. Each job is optimised individually without considering the capacity constraints. Profitable jobs at the individual level submit their optimal schedules as combinatorial bids to an auctioneer to acquire combinations of resource capacity-time units. Then, the auctioneer records the profit upper bound, resolves capacity conflicts to reach a feasible solution, records the profit lower bound, and updates the dual variables. Experimental results show that the proposed methodology can solve large-sized problems in reasonable CPU times. [ABSTRACT FROM AUTHOR]

Considering the increasing competition among manufacturers and the diversification of customer orders, it is important to use the optimal production system. Hybrid manufacturing systems have proven to be a viable solution to achieve a satisfactory level of customization while maintaining adequate production volumes. This article determines the optimal strategy for decision makers in order to choose the appropriate method of supplying customers’ orders. In this article, a hybrid MTS-MTO production system has been studied in two different scenarios. For this purpose, a mixed-integer linear bi-objective model with product substitution and replaceable materials has been developed and solved by a Lagrangian Relaxation algorithm. The findings of this study reveal that enabling product substitution by manufacturers leads to an increase in the number of accepted orders, along with resulting in a higher average level of customer dissatisfaction. The system prioritizes the fulfillment of orders from permissive customers. Also, with the increase of uncertainty in each of the supplying methods, manufacturers are using other sources to supply raw materials to satisfy the accepted orders. [ABSTRACT FROM AUTHOR]

Order acceptance and scheduling (OAS) is a critical part of production planning in manufacturing units. In real-world problems, where the manufacturing units are required to meet capacity limitations and delivery time obligations, OAS problem arises. In this study, a hybrid population-based heuristic of imperialists competitive algorithm (ICA) and well-known simulated annealing (SA) has been applied to deal with the variant of OAS with time windows and sequence-dependent setup time. As part of ICA, a specific representation has been proposed to capture all of the characteristics of an OAS solution. The neighborhood functions have been developed based on this specific representation. In order to improve conventional ICA, a roulette wheel is used for selecting these functions, which is periodically updated based on their prior effectiveness. As an additional approach to avoid being confined to a local optimum point, an escape strategy is employed. This part of the study utilizes eight local search functions that are different from those in the ICA in order to further diversify the population. The proposed algorithm has been implemented on benchmark instances, and results have been analyzed. The results of our experiments demonstrated that ICA-SA shows improvement over well-known existing methods both in terms of quality and speed. In addition, we found in 32 out of 1500 cases, ICA-SA yielded a better result than the upper bound generated by MILP. This improvement in optimal solutions was made possible due to the allowance of scheduling tasks with zero revenue in this study. Therefore by relaxing one of the constraints of MILP, better upper bounds can be achieved. [ABSTRACT FROM AUTHOR]

Purpose: This article studies an issue in the fish farming industry in which the goal is to find the best multi-period planning for handling various chains, including ordering, breeding, and selling of trout over a time horizon. Methodology: In this study, a new formulation is presented as a mixed integer linear programming model that could find the optimum solution quickly. In the new proposed formulation, some intermediate stages of the breeding chain that do not affect decisions are ignored, and therefore, the size and complexity of the proposed model reduce without compromising the optimality of the answers. Findings: After implementing the proposed model, using different data samples, it can be seen that this model achieves the optimal solution in a short time, including volume and time of spawning in each breeding chain and different periods, harvesting time, and accepting or rejecting the main demands. Originality/Value: In this paper, the issue of scheduling of fish farming chains and sales management, which there are a few studies in this field, has been studied and a new mixed integer linear programming model is presented. Compared to the previous model, this model has more realistic assumptions and less complexity and execution time. [ABSTRACT FROM AUTHOR]

This paper solves the order acceptance and scheduling (OAS) problem of customized products in make-to-order (MTO) supply chains. A new integrated framework that links supply chain operations is presented to overcome uncertainties in order variations and maximize the agility and responsiveness of those systems. A novel mixed integer programming mathematical model is proposed to optimize order acceptance, production planning, maintenance, and transportation decisions. The products are produced based on job-shop scheduling plans while considering the real-time access to available supply and distribution resources. To validate the efficiency of the proposed framework, the model is tested with a four-layer supply chain. Then, a wide range of experiments is implemented to study the effects of different factors such as order uncertainty, costs, maintenance, and customer satisfaction. The results proved that the proposed integrated order acceptance and scheduling can save up to 30% of supply chain expenses with efficient management of the supply, production, and distribution capacities. The results also showed that defining reasonable target customer satisfaction plays an important role in the success of these complex service systems. In addition, a scalability test proved the efficiency of the proposed model for decision making in large systems. [ABSTRACT FROM AUTHOR]

Rooted from the Industry 4.0 principles, Cloud Manufacturing (CMfg) is a novel customer-oriented manufacturing norm, which can assist enterprises to withstand in the nowadays highly volatile and competitive market. CMfg systems comprise two separate parties, namely, customers and factories, with independent individuals. In this regard, considering the utilities of both customers and factories and establishing the equity amongst their individuals are of particular importance for the survival and flourishment of CMfg systems. Furthermore, due to the limited capacity of resources, tightness of due dates, and customers' cost expectations, all orders may not be accepted in CMfg systems. Accordingly, this paper aims to explore a scheduling problem in a CMfg system. A multi-objective mathematical model is presented for the problem, which can determine the acceptance or rejection of orders, set prices, and schedule them in an integrated manner to maximise the customers and factories' utilities, and enhance the equity among their members. Due to the high complexity of the problem, two matheuristic methods based on the Multi-Objective Grey Wolf Optimizer (MOGWO) and Non-dominated Sorting Genetic Algorithm II (NSGA-II) are developed. An extensive computational experiment is carried out to validate the proposed matheuristic methods and evaluate their performance. Moreover, some guidance is presented for managers by conducting a sensitivity analysis. [ABSTRACT FROM AUTHOR]

We study the integration of order acceptance and capacity planning in multi-project environments with dynamically arriving projects. We model this planning problem as a continuous-time Markov decision process to determine long-term optimal decisions. We examine whethermacro-process planning should be performed before or after order acceptance. We characterise the structure of optimal policies, and explore the dependence on a number of parameters such as project payoff, project cost and order arrival time. We also look into the effects of set-up costs and the use of non-regular capacity. [ABSTRACT FROM AUTHOR]

The synchronization of production capacity with demand plays a significant role in maximizing profit. Firms attempt to align their activities, requiring that businesses be occasionally turned away. A trade-off exists between the revenue gained by a specific order and all related processing costs. We solve an important order acceptance and scheduling problem arising from this business model. A firm that receives multiple orders must determine: (i) whether to accept or reject any order, (ii) how to assign accepted orders to identical parallel machines, (iii) the sequence of accepted orders, and (iv) when to schedule order starting times. The objective is to maximize the total profit, measured as the difference between revenue and tardiness penalty costs. We propose an effective hybrid algorithm (GA-RP-TS) that combines a refining process (RP), a genetic algorithm (GA), and a tabu search (TS) step to tackle this NP-hard problem. The hybrid GA-RP-TS employs GA's strong global search ability, uses RP to improve a solution, and performs exploitation with TS. In particular, the proposed GA-RP-TS balances the intensification and diversification ability of metaheuristics and evolutionary algorithms. We evaluate the performance of the proposed GA-RP-TS by testing benchmark instances. Results show that the proposed GA-RP-TS outperforms the state-of-the-art algorithm in solving large-sized instances. GA-RP-TS obtains 22 new best solutions out of the 54 tested benchmark instances, besides helping prove optimality for two instances. In addition, GA-RP-TS consumes less computation time. Sensitivity analysis shows that the excellent performance of GA-RP-TS comes from the hybridization of its three components, i.e., GA, RP, and TS. • We develop an efficient hybrid heuristic for the order acceptance and scheduling problem. • The heuristic combines the strengths of a genetic algorithm, tabu search, and a refining process. • The performance of the heuristic is demonstrated using benchmark instances. • The heuristic successfully identifies 22 new best solutions. • The heuristic's excellence is attributed to the hybridization of its three components. [ABSTRACT FROM AUTHOR]

So far, a great bulk of research has been dedicated to demand fluctuations in the make-to-order (MTO) environment, and several solutions have been proposed. In contrast, cellular layouts have received little attention from researchers. Also, in studies on dynamic cell production systems, the concept of dynamics is limited to the possibility of making changes between periods. This paper presents a new mathematical model to create an integrated system of order prioritizing, capacity measurement, and scheduling for dealing with new orders in MTO environments arranged by a cellular manufacturing layout. In this model, it is possible to negotiate the price and delivery time. The periods are also considered to be connected, and unlike the existing dynamic cellular manufacturing systems (DCMSs), it is possible to receive an order at any moment. Moreover, machine relocation is allowed with regard to the time of relocation during the periods. Another notable feature is the alternative routing for parts and cell formation (CF) at the same time as scheduling. The proposed model has two objectives including a) maximizing the profit and b) maximizing the number of orders accepted based on their priorities. The bi-objective model for small sizes has been solved by GAMS software using the augmented epsilon-constraint method. The effects of key parameters as well as some important features of the model, such as the possibility of checking the acceptance/rejection of a new order during the program, are addressed. Generally, the proposed model is able to develop a DCMS in an online cellular manufacturing system (OCMS). The results of sensitivity analysis show that the integration of CF, GS, order acceptance, pricing, and delivery time in a mathematical model simultaneously can significantly improve system performance and increase system profits. Finally, an NSGA-II algorithm is developed to solve the problem in larger sizes. To verify the computational effectiveness of the employed NSGA-II in comparison with a CPLEX solver, its performance is evaluated using two methods. [ABSTRACT FROM AUTHOR]

In this paper, we investigate a manufacturer selection and composition problem for Distributed Virtual Manufacturing Network (DVMN) with order acceptance and scheduling of deteriorating jobs, where potential manufacturers include proprietary plants and outsourced co-manufacturers. In such a problem, at the beginning of planning horizon, a manufacturing company (MC) receives a series of order requests from its customers. Due to the limited production capacity, the MC is not able to fulfill all the orders requested by the customers on time. To satisfy the order demand as much as possible, one of the MC's choices is to establish a collaborative manufacturing platform (i.e., DVMN) that leverages the advantages of a social manufacturing network to expand their production capabilities. However, the increase in production capacity does not mean that all orders from the customers are able to be accepted and produced. In fact, standing at the profit-maximizing point of view, the order requests that do not bring profits to the company need to be rejected at the beginning of planning horizon. For the accepted orders, the MC will produce a part of them by its plants in the DVMN, and the remaining orders will be outsourced to other co-manufacturers in the DVMN. Based on some structural properties, we develop a novel Reduced Variable Neighborhood Search (RVNS) algorithm incorporated multiple random mutations neighborhood structures (NSs-MRM) to determine the composition of the DVMN, and further determine which orders should be rejected, which accepted orders should be processed by the company-owned plants in the DVMN, and which accepted orders should be outsourced to the co-manufacturers in the DVMN. In particular, the processing sequence of the orders produced in the company-owned plants are also determined. Finally, a reasonable composition of the DVMN and corresponding order acceptance as well as order scheduling on each member of the DVMN are given. To evaluate the proposed algorithm, a series of computational experiments are conducted, and the experimental results show that the proposed algorithm outperforms other existing baseline algorithms in solving both small-scale and large-scale problem instances. [ABSTRACT FROM AUTHOR]

This research focuses on an Order Acceptance Scheduling (OAS) problem on a single machine under time-of-use (TOU) tariffs and taxed carbon emissions periods with the objective to maximize total profit minus tardiness penalties and environmental costs. Due to the NP-hardness of the considered problem especially in presence of sequence-dependent setup-times, two fix-and-relax (FR) heuristics based on different time-indexed (TI) formulations are proposed. A metaheuristic based on the Dynamic Island Model (DIM) framework is also employed to tackle this optimization problem. These approached methods show promising results both in terms of solution quality and solving time compared to state-of-the-art exact solving approaches. [ABSTRACT FROM AUTHOR]

This paper studies the order acceptance and scheduling problem under a single machine environment when the orders come stochastically during the planning horizon and a sequence-dependent setup time is required between the processing of different types of orders. The objective is to maximise the expected revenue subject to the due date constraints. The problem is formulated as a stochastic dynamic programming model. A rule based on the opportunity cost of the remaining system capacity for the current system state is proposed to make the order acceptance decisions. The remaining system capacity is estimated by a heuristic which generates a good schedule for the accepted orders. Its opportunity cost is estimated by both mathematical programme and greedy heuristic. Computational experiments show that the profit generated by the integrated dynamic programming decision model is much higher than the widely used first-come-first-accept policy in industries and the benefit increases with the length of planning horizon, the arrival rate and the length of lead time. Acceptance decision based on mathematical programming outperforms greedy heuristic by about 7% and its computational time is short. It also shows that the quality of the solutions generated by the opportunity cost based order acceptance rule is satisfactory. [ABSTRACT FROM PUBLISHER]

We consider a tactical planning problem, which integrates production planning decisions together with order acceptance decisions, while taking into account the dependency between workload and lead times. The proposed model determines which orders to accept and in which period they should be produced, so that they can be delivered to the customer within the acceptable flexible due dates. When the number of accepted orders increases, the workload and production lead time also increase, and this may result in the possibility of missing customer due dates. This problem is formulated as a mixed integer linear programme for which two relax-and-fix heuristic solution methods are proposed. The first one decomposes the problem based on time periods, while the second decomposes it based on orders. The performances of these heuristics are compared with that of a state-of-the-art commercial solver. Our results show that the time-based relax-and-fix heuristic outperforms the order-based relax-and-fix heuristic, and the solver solution as it yields better integrality gaps for much less CPU effort. [ABSTRACT FROM AUTHOR]

The lead time (LT) quotation and pricing models in make-to-order environments usually assume that all arriving orders are accepted. Hence, despite the loss of demand, the firm must quote a relatively long LT to tackle the situations where there are too many waiting orders. This paper investigates the policy of accepting orders based on the state of the system and, consequently, rejecting customers when the number of waiting orders is larger than a given threshold value. Indeed, if there are less than K jobs in the system (K fixed), then the new order is accepted and a pair of LT and price are quoted to the customer. Otherwise, the order is rejected. Thus, we model the firm's operations as a M/M/1/K queue instead of the commonly-used M/M/1 queue. We determine the optimal solution for K = 1 and solve the problem numerically for K ≥ 2. We show that our policy can be more profitable than the all-orders acceptance policy. We provide insights into when should a K be imposed and how such a K should be chosen. We find that the higher the sensitivity of demand to price or to LT is, the lower the optimal value of K. [ABSTRACT FROM AUTHOR]

We study the problem of order acceptance, scheduling and pricing (OASP) in parallel machine environment. Each order is characterized by due date, release date, deadline, controllable processing time, sequence dependent set up time and price in MTO system. We present a MILP formulation to maximize the net profit. Then under joint optimization approach, the pricing decisions set for unrelated parallel machine environment. The results show that the basic developed problem can solve the scheduling decisions based on different levels of products’ priced. Thus the problem solves these two categories of decisions simultaneously. Moreover, the changes of accepted orders in pricing levels can be analyzed regarding its dependency to price elasticity of items for future research.

Order acceptance and scheduling problem consists of simultaneously deciding which orders to be selected and how to schedule these selected orders. An extension of this problem was introduced by Oğuz, Salman & Bilgintürk in 2010 and a mathematical formulation was presented. They defined the problem with sequence-dependent setup times and release dates. In this paper, we consider the case where there are no release dates for all orders. We develop a new mathematical formulation with O(n2) binary variables and O(n2) constraints and conduct a detailed computational analysis with CPLEX 12.4 by solving benchmark instances proposed by Cesaret, Oğuz, & Salman (2012). Reduced formulation of Oğuz, Salman & Bilgintürk (2010) can solve the test problems up to 10 orders to optimality in given time limit. Our proposed formulation can solve all the available instances up to 100 orders to optimality within the same time limit. We observe that our formulation is extremely faster than the existing one and can solve small and moderate size real-life problems to optimality.

This paper focuses on a make-to-order production system, where rejection of some orders is inevitable due to limited production capacity. In such a system, accepting all orders may cause overloads, order delays, and customer dissatisfaction. For this reason, firms tend to reject some orders. The order acceptance and scheduling problem is defined as deciding simultaneously which orders to be selected and how to schedule these selected orders. An extension of this problem with sequence-dependent setup times and release dates has been rarely studied, and the existing studies suggest that setup activities wait for the release date to be performed. However, in real production environments this may not be the case. Therefore, this paper examines the relationships between setup times and release dates considering the overall scheduling literature. Previous scheduling studies define two different relationships concerning setup times and release dates. One of them considers setup time is completely dependent on release date, and the other one claims that they are completely independent. In this paper, a new relationship is addressed to propound that setup time may be partially dependent on the release date. The paper also proposes a new mixed integer linear programming formulation with O(n2) binary decision variables and O(n2) constraints. It includes a detailed computational analysis by solving available instances in the literature, which suggests that existing formulation can solve the test problems to optimality with up to 10 orders in a given time limit. Our proposed formulation, however, can solve the test problems to optimality with up to 50 orders within the same time limit. According to the findings, our approach seems to be more suitable for real-life applications, and the proposed formulation is extremely faster than the existing one. [ABSTRACT FROM AUTHOR]

This paper considers a joint order acceptance and scheduling problem under a general scenario. A manufacturer receives multiple orders with a given revenue, processing time, release date, due date, deadline, and earliness and tardiness penalties. The manufacturer can be seen as a single-machine system. Due to limited capacity, the manufacturer cannot process every order and needs to determine the optimal set of accepted orders and corresponding production schedule such that the total profit is maximized. The manufacturer can extend its capacity with overtime by paying an additional cost. A time-indexed formulation is presented to model the problem. Two exact algorithms are proposed. The first algorithm, denoted by DPIA-GR, is a dynamic programming (DP)-based algorithm that starts by solving a relaxed version of the original model and successively recovers the relaxed constraint until an optimal solution to the original problem is achieved. The second algorithm, denoted by DPIA-LRGR, improves DPIA-GR by incorporating Lagrangian relaxation (LR). The subgradient method is employed to find the optimal Lagrangian multipliers. The relaxed model in DPIA-GR and the LR model in DPIA-LRGR can be represented using a weighted di-graph. Both algorithms are equivalent to finding the longest path in the graph and applying a graph reduction strategy to prevent unnecessary computational time and memory usage. A genetic algorithm (GA) is also proposed to solve large-scale versions of the problem. Numerical experiments show that both DPIA-GR and DPIA-LRGR solve the problem efficiently and outperform CPLEX and GA, but DPIA-LRGR offers better performance. [ABSTRACT FROM AUTHOR]

As an innovative and successful manufacturing mode in Japanese production practice, seru production has attracted extensive attention due to its high efficiency, high flexibility, and quick responsiveness. This paper focuses on the order acceptance and scheduling problem in seru production system considering worker assignment simultaneously, where the worker's learning curve effect is also concerned because it is also an important issue for the stable performance of the entire production system. A bi-level programming model is built where the worker assignment on the upper level is to maximize worker's skill proficiency increments, while the lower level is to maximize the total net revenue by finding out the optimal order acceptance results and scheduling plan. Then, a bi-level nesting genetic algorithm (BNGA) is developed according to the natural structure of proposed mathematical model. Finally, a numerical example is applied to illustrate the feasibility and validity of the bi-level model and BNGA. [ABSTRACT FROM AUTHOR]

This research focuses on an Order Acceptance Scheduling (OAS) problem on a single machine under time-of-use (TOU) tariffs and taxed carbon emissions periods with the objective to maximize total profit minus tardiness penalties and environmental costs. Due to the NP-hardness of the considered problem especially in presence of sequence-dependent setup-times, two fix-and-relax (FR) heuristics based on different time-indexed (TI) formulations are proposed. A metaheuristic based on the Dynamic Island Model (DIM) framework is also employed to tackle this optimization problem. These approached methods show promising results both in terms of solution quality and solving time compared to state-of-the-art exact solving approaches.

Order acceptance and scheduling problem consist of simultaneously deciding which orders to be selected and how to schedule these selected orders. An extension of the sequence-dependent setup times and release dates was introduced in 2010 and a mathematical formulation was presented. Since then, a few mathematical formulations have appeared in the literature by addressing this problem on the basis of sequence-dependent setup times. However, some of the mathematical formulations are nonlinear or lack usability. Therefore, the model presented in 2010 is still being considered in recent studies. In this paper, we investigated the case in which there are sequence-dependent setup times with no release dates for all orders. We developed a new mathematical formulation with O(n²) binary variables and O(n²) constraints. In order to see the performance of our formulation, we conducted a computational analysis with CPLEX 12.4 by solving benchmark instances available in the literature. To manage the comparison, we reduced the existing formulation to the without release dates for all orders. As a result, we observed that the existing formulation can solve the test problems with up to 10 orders in a given time limit. On the other hand, our proposed formulation can solve all the available instances with up to 100 orders within the same time limit. Our proposed formulation is extremely faster than the existing one and can solve small and moderate sized real-life problems in a reasonable time. Thus, the researchers do not need any special heuristics for solving such problems. Instead, they can directly use our formulation with an optimizer. [ABSTRACT FROM AUTHOR]

Capacity coordination, as the tactical level of hierarchical production planning in hybrid MTS/MTO systems, includes numerous important decisions. In this paper, two of these decisions i.e. finding the best strategy for the acceptance/rejection of incoming orders and determining orders' due dates - are investigated. Also a simulation model is proposed to evaluate the efficiency of the presented mixed integer model. Finally, an industrial case study is considered in a food processing plant to evaluate the proposed framework and conduct suitable sensitivity analysis. [ABSTRACT FROM AUTHOR]

Abstract The importance, benefits, and impact of integration of decisions within supply chains have long been investigated by many researchers. Order acceptance and supplier selection are two of the most critical decisions for supply chain managers. Throughout the process of order acceptance, a manufacturer has to decide which orders to be accepted and processed and based on the accepted orders, the volume of required raw material is determined. On the other hand, a manufacturer aims to choose one or several suppliers among all possible choices to provide sufficient raw material for the accepted orders, subject to different criteria such as list price, transportation cost, etc. This paper addresses an integrated framework for profit maximization in an integrated supplier selection, order acceptance and scheduling problem in a single-machine environment with multiple customers. There is substantial literature on the problems of supplier selection and order acceptance; however, to the best of our knowledge, this paper is the first research that integrates these essential decisions in the form of a mathematical model to maximize the total profit. The problem is NP-hard in nature; therefore, solving to optimality is not practically possible for problems with medium and large size. For that purpose, we developed a Heuristic Algorithm (HA) to solve the problem above in a reasonable time, with proper accuracy. Results from this heuristic algorithm are compared with that of a commercial solver (GAMS) and the well-known Genetic Algorithm (GA) and Variable Neighborhood Search (VNS). Computational experiments demonstrate that the developed heuristic algorithm is more efficient in comparison with other tested methods. Highlights • A model is developed to integrate order acceptance, order scheduling, due date assignment, and supplier selection decisions. • A heuristic algorithm is developed to solve the problem promptly with great accuracy. • Extensive computational experiments and analysis demonstrate the outperformance of the developed heuristic algorithm. [ABSTRACT FROM AUTHOR]

Highlights • An improved Water Flow-like Algorithm for order acceptance and scheduling with identical parallel machine problem. • The characteristics consider due dates, order profits, tardiness penalties, sequence-dependent setup times. • Neighborhood search method with different precipitation mechanisms is proposed to overcome local optimal solutions. Abstract Order acceptance and scheduling (OAS) problem denotes manufactures in a make-to-order environment have to determine which orders should be accepted and how to arrange the accepted orders in a processing sequence. If the order schedule is not well-arranged, that will lead to the tardiness penalties and the impact of other orders on the allocation of resources. Therefore, this study considered the due dates, order profits, tardiness penalties, sequence-dependent setup times and proposed the integrated model for the problem of order acceptance and identical parallel machine scheduling. The modified Water Flow-like Algorithm (WFA) was developed to solve the problem. The proposed water flow-like algorithms, WFA.I and WFA.II are compared, with a well-defined classic algorithm, particle swarm optimization embedded with variable neighborhood search, to find out their solution qualities in different sizes of problems. The computational results show that, in the large-sized problems, WFA.II is performing much better than WFA.I with a range of solution gaps, 0.45% ∼38.06%. WFA.II is also competitive as the well-defined classic algorithm, PSOVNS, in the large-sized problems, with an extremely short range of solution gap, 0.17% ∼1.65%. [ABSTRACT FROM AUTHOR]

This paper proposes a novel approach of coordinating decisions in an integrated supply chain (ISC): coordinating order acceptance (OA) and batch delivery (BD) due to round trip transportation (RTT) and using third-party logistics (3PL) vehicles. The paper aims at trading off among accepted orders revenue, delivery costs as well as any penalties incurred in the ISC to maximize the total benefit. A novel mixed-integer programming is proposed for the problem. In addition, the paper provides a heuristic to form batches and develops a hybrid evolutionary computation algorithms based on particle swarm optimization (PSO) and genetic algorithm (GA) to solve the problem. An information sharing mechanism is improved and applied. To explore and to locate the proposed PSO in a better neighborhood, a local search is proposed. Taguchi experimental design is utilized to set the appropriate values of the algorithms’ parameters and random instances are generated to evaluate the performance of the algorithms. The paper investigates the profitability sensitivity of the problem to parameters and analyzes the effect of the changes in the parameters on the performance of our proposed algorithms. The attained results show the appropriate performance of our algorithms. [ABSTRACT FROM AUTHOR]

In a single supply chain scenario, orders are likely to be refused for lack of insufficient capacity and production time. In this paper, cluster supply chains (a kind of multiple supply chains, short for CSC) is introduced to avoid this potential operational risk via across-chain cooperation, which is not considered in any previous work. First, the framework of order selection in cluster supply chain (CSC) is presented based on four order categories (direct order, reserve order, across-chain order and rejected order), followed by that the model without and with across-chain cooperation in cluster supply chains are proposed to aid operational managers to make joint decision regarding order acceptance and scheduling under maximizing the overall profit. Considering the complexity of cluster supply chains structure and a mass of data from actual operations, a parallel Lagrange heuristic algorithm is devised to solve the Mixed-Integer Non-Linear Program (MINLP) problem. Meanwhile, Benders algorithm is utilized to compare with it for evaluating performance. The result proves the parallel Lagrange heuristic algorithm outperforms Benders approach, the former can efficiently solve large-scale-data problem instances at relatively short time. The outcomes also reveal that, by designing the different combination of the factor of rejected order and that of across-chain order, it can be better trade-off between order due-date and cost while better aligning with the long-term business strategy in cluster supply chains. [ABSTRACT FROM AUTHOR]

The way orders are accepted or rejected is the most important factor in customer satisfaction and success of make-to-order systems. The incoming orders to such organizations have certain delivery date in which the customer expects the order to be fulfilled and delivered. In some cases, unexpectedly increased orders exceed the existing capacity for on time fulfillment. In addition to rejection of order, as a typical choice, other options like outsourcing and capacity expansion are available to compensate for capacity shortage and deliver incoming orders according to schedule. However, each of the proposed options is superior in one or more criteria and so selecting the best one is not simply possible. The main goal of this study is to provide managers with a comprehensive, systematic, and applicable approach to evaluate and select the best of the existing options. For this purpose, a model comprised of some multi-criteria techniques is delivered. Our proposed model is a blend of FAHP and FTOPSIS methods. In this model, FAHP is first used to determine the weight of criteria and then Fuzzy-TOPSIS (FTOPSIS) is employed to rank the options. Finally, the proposed model is applied on an actual case to assess and examine its efficiency.

In this paper, we studysimultaneouslyorder acceptance and scheduling in a single machine environment. We assume the capacity of accepted orders is limited and the orders are characterized based on due dates, processing times, revenues, weights, and sequence-dependent setup times. The objective is profit maximization that is the total revenues minus total weighted tardiness. We propose a mathematical programming model and two population-based metaheuristic algorithms, biogeography-based optimization (BBO) algorithm, and genetic algorithm (GA), for solving this problem. We use Taguchi design to determine parameters values. The results of two developed algorithms are not only compared to the results of the mathematical model, but also compared to each other for determining the best algorithm. Computational results on generated instances show that the BBO algorithm outperforms GA, particularly for large size instances, in terms of the objective function. [ABSTRACT FROM AUTHOR]

While there are many mathematical approaches to order acceptance (OA), the performance of OA in practice is overlooked. This paper assesses current OA methods and performance measures that could measure OA. The outcome is a conceptual performance measurement system for OA that measures performance through utilization of resources, output and flexibility in a diagnostic and predictive manner. This paper found a close relationship between OA and S&OP, as they both match supply and demand. The contribution of this paper is that it detaches OA from mathematical models and proposes a method to assess the value creation of OA in practice. [ABSTRACT FROM AUTHOR]

The aim of this work is to bridge the gap between the theory and actual practice of production scheduling by studying a problem from a real-life production environment. This paper considers a practical Sanitaryware production system as a number of make-to-order permutation flowshop problems. Due to the wide range of variation in its products, real-time arrival of customer orders, dynamic batch adjustments, and time for machine setup, Sanitaryware production system is complex and also time sensitive. In practice, many such companies run with suboptimal solutions. To tackle this problem, in this paper, a memetic algorithm based real-time approach has been proposed. Numerical experiments based on real data are also been presented in this paper. [ABSTRACT FROM PUBLISHER]

The customer's order acceptance and scheduling problem in a single machine environment has long been an appealing research subject. In this paper, a situation where a pool of customers exists and each customer tends to place all his orders to a single company is addressed. Hence, the customer's orders will be entirely either accepted or rejected. In this work, decisions on rejection or acceptance of customers and sequencing of the accepted orders are simultaneously considered. The goal is to maximize the total net profit obtained from accepted orders revenues contributed by tardiness penalty. In response to the computational complexity of the problem, a heuristic algorithm and two optimal branch and bound procedures with upper bound, lower bound, and dominance rules are developed. Computational results demonstrate that the proposed methods perform well in a timely manner. [ABSTRACT FROM AUTHOR]

Additive manufacturing (AM) is increasingly used in industrial production. Compared to conventional manufacturing technologies, such as milling and casting, AM offers a high degree of design freedom. Nevertheless, still some manufacturing restrictions and design guidelines have to be considered to ensure a flawless production. Therefore, a checking of design guidelines is a necessary step in order acceptance. Addressing this need, this paper presents an integrated analysis of design guidelines for an automated order acceptance. In recent times, guideline catalogs for the design of additively manufactured parts have been developed. However, the analysis of a part's geometry with regard to these guidelines still requires a lot of manual work and expert knowledge. This paper introduces different algorithmic approaches, which automate the analysis and assessment of a part's geometry. Based on a preselection of guidelines from existing design catalogs for selective laser melting and sintering, this paper presents algorithms to automatically check the manufacturability of a part. The algorithms use the triangulated surface geometry (STL) of a part. They are implemented within a web-based platform for the automated order acceptance of additive manufactured parts. The evaluation compares the different algorithms regarding their efficiency and effectiveness. [ABSTRACT FROM AUTHOR]

This paper addresses a production scheduling problem in a single-machine environment, where a job can be either early, on time, late, or rejected. In order acceptance and scheduling contexts, it is assumed that the production capacity of a company is overloaded. The problem is therefore to decide which orders to accept and how to sequence their production. In contrast with the existing literature, the considered problem jointly takes into account the following features: earliness and tardiness penalties (which can be linear or quadratic), sequence-dependent setup times and costs, rejection penalties, and the possibility of having idle times. The practical relevance of this new NP-hard problem is discussed and various solution methods are proposed, ranging from a basic greedy algorithm to refined metaheuristics (e.g., tabu search, the adaptive memory algorithm, population-based approaches loosely inspired on ant algorithms). The methods are compared for instances with various structures containing up to 200 jobs. For small linear instances, the metaheuristics are favorably compared with an exact formulation using CPLEX 12.2. Managerial insights and recommendations are finally given. [ABSTRACT FROM AUTHOR]

Decisions related to acceptance or rejection of orders play an important role in companies engaged in make-to-order production. The incoming orders have a specific delivery date by which the customer expects the due date to be met and the order delivered. In some cases the level of input orders exceeds beyond the existing capacity. In such situations the main concern is to decide which orders must be accepted and which ones rejected taking into account the available production capacity. This paper prioritises the input orders according to a comprehensive and systematic multi criteria decision making (MCDM) model. It then proceeds with making decisions to either accept or reject orders according to the calculated prioritises and production constraints. Ultimately the optimum list of orders for acceptance is determined. The proposed model is a combination of two techniques of Fuzzy Analytical Hierarchy Process (FAHP) and Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS). In this model FAHP is used to determine the weights of criteria and TOPSIS is used for prioritizing the orders. Finally the proposed model is tested for its efficiency by application to a real case.